WO2020158095A1 - Evaluation device - Google Patents

Evaluation device Download PDF

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Publication number
WO2020158095A1
WO2020158095A1 PCT/JP2019/043656 JP2019043656W WO2020158095A1 WO 2020158095 A1 WO2020158095 A1 WO 2020158095A1 JP 2019043656 W JP2019043656 W JP 2019043656W WO 2020158095 A1 WO2020158095 A1 WO 2020158095A1
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WO
WIPO (PCT)
Prior art keywords
evaluation
reproduction
unit
moving image
processing device
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PCT/JP2019/043656
Other languages
French (fr)
Japanese (ja)
Inventor
一輝 浅井
潤相 呉
銀平 岡田
Original Assignee
株式会社Ｎｔｔドコモ
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Application filed by 株式会社Ｎｔｔドコモ filed Critical 株式会社Ｎｔｔドコモ
Publication of WO2020158095A1 publication Critical patent/WO2020158095A1/en

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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F11/00—Error detection; Error correction; Monitoring
    • G06F11/30—Monitoring
    • G06F11/34—Recording or statistical evaluation of computer activity, e.g. of down time, of input/output operation ; Recording or statistical evaluation of user activity, e.g. usability assessment
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F11/00—Error detection; Error correction; Monitoring
    • G06F11/36—Preventing errors by testing or debugging software
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
    • H04N21/00—Selective content distribution, e.g. interactive television or video on demand [VOD]
    • H04N21/20—Servers specifically adapted for the distribution of content, e.g. VOD servers; Operations thereof
    • H04N21/25—Management operations performed by the server for facilitating the content distribution or administrating data related to end-users or client devices, e.g. end-user or client device authentication, learning user preferences for recommending movies
    • H04N21/258—Client or end-user data management, e.g. managing client capabilities, user preferences or demographics, processing of multiple end-users preferences to derive collaborative data
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
    • H04N5/00—Details of television systems
    • H04N5/76—Television signal recording
    • H04N5/91—Television signal processing therefor
    • H04N5/93—Regeneration of the television signal or of selected parts thereof

Definitions

  • the present invention relates to an evaluation device.
  • Patent Document 1 describes a user terminal that reproduces a moving image based on moving image data transmitted by a distribution site.
  • a user terminal as described in Patent Document 1 reproduces a moving image by executing software for reproducing the moving image based on the moving image data (hereinafter referred to as “moving image reproducing software”).
  • the video playback software is provided to the user terminal after passing the test in the development stage.
  • An object of the present invention is to provide a technology that can reduce the time and effort required for testing the moving image playback software during the development stage.
  • An evaluation device uses a learning model that has learned relational data indicating a relationship between a situation regarding reproduction of a moving image and an evaluation for the situation, and performs reproduction regarding reproduction of a moving image performed by software for reproducing a moving image.
  • An evaluation unit that evaluates the situation and an output unit that outputs information based on the evaluation result of the evaluation unit are included.
  • FIG. 1 is a diagram showing an overall configuration of an evaluation system 1 according to the first embodiment.
  • the evaluation system 1 evaluates the reproduction software at the development stage of the reproduction software for reproducing the moving image and the sound.
  • the playback software is an example of moving image playback software.
  • the evaluation system 1 includes a distribution server 10, an information processing device 20, and an evaluation device 30.
  • the distribution server 10, the information processing device 20, and the evaluation device 30 can communicate with each other via the network NW.
  • the number of each of the distribution server 10, the information processing device 20, and the evaluation device 30 is not limited to “1”.
  • the evaluation system 1 may include a plurality of distribution servers 10, a plurality of information processing devices 20, and a plurality of evaluation devices 30.
  • the distribution server 10 transmits distribution data including moving image data indicating a moving image and sound data indicating sound to the user device 20.
  • the distribution data is, for example, data representing a movie.
  • the distribution data is not limited to data representing a movie, but may be data representing a television program, for example.
  • Each of the moving image data and the audio data is encoded data.
  • the reproduction timing of the moving image based on the moving image data and the reproduction timing of the sound based on the audio data are defined in advance.
  • the frame rate of the moving image indicated by the moving image data is fixed to the reference frame rate.
  • the reference frame rate is, for example, 30 fps (Frames Per Second).
  • the reference frame rate is not limited to 30 fps and may be higher or lower than 30 fps.
  • the frame rate of the moving image indicated by the moving image data may be changed.
  • the distribution server 10 adjusts the bit rate of distribution data in response to a request from the user device 20.
  • the distribution server 10 is composed of a computer system including a communication device 101, a storage device 102, and a processing device 103.
  • the term “device” in a communication device, a storage device, a processing device, an input device described below, and a display device described below may be replaced with another term such as a circuit, a device, or a unit.
  • the respective elements of the distribution server 10 are mutually connected by a single bus or a plurality of buses. Each element of the distribution server 10 is composed of one or more devices.
  • the communication device 101 communicates with another device, for example, the information processing device 20, via the network NW.
  • the communication device 101 is also called, for example, a network device, a network controller, a network card, or a communication module.
  • the storage device 102 is a recording medium that can be read by the processing device 103.
  • the storage device 102 stores a plurality of programs including a control program executed by the processing device 103, various data used by the processing device 103, and various distribution data.
  • the storage device 102 is configured by at least one of a recording medium such as a ROM (Read Only Memory), an EPROM (Erasable Programmable ROM), an EEPROM (Electrically Erasable Programmable ROM), and a RAM (Random Access Memory).
  • the processing device 103 is a processor that controls the distribution server 10.
  • the processing device 103 is composed of, for example, one or more chips.
  • the processing device 103 includes an interface with peripheral devices and a central processing unit (CPU).
  • the central processing unit includes an arithmetic unit, a register and the like. Even if some or all of the functions of the processing device 103 are executed by hardware such as DSP (Digital Signal Processor), ASIC (Application Specific Integrated Circuit), PLD (Programmable Logic Device), FPGA (Field Programmable Gate Array), etc. Good.
  • the processing device 103 executes various processes in parallel or sequentially.
  • the processing device 103 reads the control program from the storage device 102.
  • the processing device 103 functions as the distribution control unit 110 by executing the control program.
  • the delivery control unit 110 is an example of a functional block of the processing device 103.
  • the distribution control unit 110 may be configured by hardware such as DSP, ASIC, PLD, or FPGA.
  • the delivery control unit 110 controls transmission of delivery data.
  • the distribution control unit 110 transmits distribution data to the information processing device 20 in response to a request from the information processing device 20.
  • the distribution control unit 110 also adjusts the bit rate of the moving image data included in the distribution data in response to a request from the information processing device 20.
  • the bit rate of moving image data represents the amount of moving image data transmitted per second.
  • One second is an example of unit time.
  • the unit time is not limited to 1 second and may be longer or shorter than 1 second.
  • the information processing device 20 is, for example, a smartphone.
  • the information processing device 20 is not limited to the smartphone.
  • the information processing device 20 may be a notebook computer, a wearable terminal, a tablet terminal, a desktop computer, or the like.
  • the information processing device 20 receives the distribution data from the distribution server 10. Playback software at the development stage is installed in the information processing device 20.
  • the information processing device 20 reproduces the moving image and the sound by processing the distribution data using the reproduction software in the development stage.
  • the information processing device 20 is configured by a computer system including an input device 201, a display device 202, a speaker 203, a communication device 204, a storage device 205, and a processing device 206. Each element of the information processing device 20 is mutually connected by one or more buses. Further, each element of the information processing device 20 is configured by one or more devices.
  • the input device 201 is a device for inputting information used by the information processing device 20 to the information processing device 20.
  • the input device 201 receives user input such as instructions or questions from the user.
  • the input device 201 receives an operation for inputting codes such as numbers and letters to the information processing device 20 and an operation for selecting an icon displayed by the display device 202.
  • the input device 201 is, for example, a touch panel that detects contact with the display surface of the display device 202.
  • the input device 201 is not limited to the touch panel.
  • the input device 201 may be a keyboard, a mouse, a switch, a button, or the like.
  • the display device 202 displays various images under the control of the processing device 206.
  • the display device 202 displays the icon and the moving image based on the moving image data included in the distribution data at the same time or at different timings.
  • the display device 202 is, for example, a liquid crystal display panel.
  • the display device 202 is not limited to the liquid crystal display panel.
  • the display device 202 may be an organic EL (Electro Luminescence) display panel or the like.
  • the speaker 203 outputs various sounds under the control of the processing device 206. For example, the speaker 203 outputs a sound based on the sound data included in the distribution data.
  • the communication device 204 communicates with other devices, for example, the distribution server 10 and the evaluation device 30 via the network NW.
  • the communication device 204 is also called, for example, a network device, a network controller, a network card, or a communication module.
  • the storage device 205 is a recording medium that can be read by the processing device 206.
  • the storage device 205 stores a plurality of programs executed by the processing device 206 and various data used by the processing device 206.
  • the plurality of programs executed by the processing device 206 include reproduction software at the development stage.
  • the storage device 205 includes, for example, a non-volatile memory and a volatile memory.
  • the non-volatile memory is, for example, ROM, EPROM, or EEPROM.
  • the volatile memory is, for example, a RAM.
  • the volatile memory included in the storage device 205 is used as a working memory of the processing device 206.
  • the processing device 206 is a processor that controls the information processing device 20.
  • the processing device 206 includes, for example, one or more chips.
  • the processing unit 206 is configured by a central processing unit (CPU).
  • CPU central processing unit
  • Some or all of the functions of the processing device 206 may be realized by hardware such as DSP, ASIC, PLD, or FPGA.
  • the processing device 206 executes various processes in parallel or sequentially.
  • the processing device 206 reads a plurality of programs from the storage device 205.
  • the processing device 206 functions as the reproduction executing unit 210 and the operation control unit 220 by executing the plurality of programs.
  • the processing device 206 reads the reproduction software at the development stage from the storage device 205.
  • the processing device 206 functions as the reproduction executing unit 210 by executing the reproduction software at the development stage.
  • the reproduction execution unit 210 and the operation control unit 220 are examples of functional blocks of the processing device 206.
  • the operation control unit 220 may be configured by hardware such as DSP, ASIC, PLD or FPGA.
  • the reproduction execution unit 210 is realized based on reproduction software at the development stage.
  • the reproduction execution unit 210 reproduces a moving image and a sound based on the distribution data.
  • the reproduction executing unit 210 decodes distribution data received from the distribution server 10 by the communication device 204, specifically, each of moving image data and audio data.
  • the reproduction executing unit 210 provides the display device 202 with the image information that is the decoding result of the moving image data, thereby causing the display device 202 to display the moving image indicated by the moving image data.
  • the reproduction executing unit 210 provides the speaker 203 with the audio information that is the decoding result of the audio data, so that the speaker 203 outputs the audio indicated by the audio data.
  • the reproduction executing unit 210 first decrypts the distribution data. Subsequently, the reproduction executing unit 210 generates image information and audio information by decoding the decrypted distribution data.
  • DRM Digital Rights Management
  • the operation control unit 220 controls the operation of the information processing device 20. For example, the operation control unit 220 transmits, from the communication device 204 to the evaluation device 30, a reproduction log indicating a situation regarding reproduction of a moving image in the information processing device 20.
  • the reproduction log is an example of status information.
  • the playback log indicates the status of the number of unplayed image frames (hereinafter referred to as “non-playback image frames”) among the consecutive image frames that make up the video based on the video data, as the status related to the video playback.
  • the reproduction log indicates the number of non-reproduction image frames, for example, the number of continuous non-reproduction image frames (hereinafter referred to as “number of consecutive frame drops”).
  • the number of consecutive frames dropped can also be referred to as “the number of consecutive unreproduced image frames”. As an example, if the image frames with index numbers “3” to “8” are not played back among the nine consecutive image frames with index numbers “1” to “9”, the playback log is "6" is shown as the number of drops.
  • the reproduction log indicates a situation regarding the reproduction of the moving image
  • sound and moving image a time difference between the reproduction timing of the sound to be reproduced together with the moving image (the sound reproduced based on the sound data) and the moving image (hereinafter referred to as “sound and moving image”).
  • sound and moving image a time difference between the reproduction timings of the audio and the moving image means the discrepancy time between the reproduction timing of the audio and the actual reproduction timing of the moving image to be reproduced in synchronization with the reproduction timing of the audio.
  • the time difference between the reproduction timings of the sound and the moving image is an example of the situation of the difference between the reproduction timings of the sound and the moving image.
  • the status of the number of unplayed image frames, the number of consecutive frames dropped, the status of audio/video playback timing deviation, and the audio/video playback timing deviation time are each related to video playback disturbance. Is an example.
  • the reproduction log of the information processing device 20 indicates the number of consecutive frames dropped and the time difference between the reproduction timings of the audio and the moving image.
  • the reproduction log of the information processing device 20 will be referred to as “evaluation reproduction log”.
  • Evaluation device 30 The evaluation device 30 automatically evaluates the reproduction software at the development stage based on the reproduction log for evaluation (reproduction log of the information processing device 20).
  • the evaluation device 30 is composed of a computer system including a communication device 301, a display device 302, a speaker 303, a storage device 304, and a processing device 305.
  • the elements of the evaluation device 30 are connected to each other by one or more buses.
  • each element of the evaluation device 30 is configured by one or two or more devices.
  • the communication device 301 communicates with another device, for example, the information processing device 20, via the network NW.
  • the communication device 301 is also called, for example, a network device, a network controller, a network card, or a communication module.
  • the display device 302 displays various images under the control of the processing device 305.
  • the display device 302 displays an image showing the evaluation result of the reproduction software at the development stage.
  • the display device 302 is, for example, a liquid crystal display panel.
  • the display device 302 is not limited to a liquid crystal display panel.
  • the display device 302 may be an organic EL display panel or the like.
  • the speaker 303 outputs various sounds under the control of the processing device 305.
  • the speaker 303 informs the evaluation result of the reproduction software at the development stage by sound.
  • the speaker 303 may be omitted.
  • the storage device 304 is a recording medium that can be read by the processing device 305.
  • the storage device 304 stores a plurality of programs including a control program executed by the processing device 305, and various data used by the processing device 305.
  • the storage device 304 also stores a learning model 304a including a plurality of coefficients K.
  • the storage device 304 is configured by at least one of recording media such as ROM, EPROM, EEPROM, and RAM.
  • the processing device 305 is a processor that controls the evaluation device 30.
  • the processing device 305 includes, for example, one or more chips.
  • the processing device 305 is configured by a central processing unit (CPU).
  • CPU central processing unit
  • Some or all of the functions of the processing device 305 may be realized by hardware such as DSP, ASIC, PLD, or FPGA.
  • the processing device 305 executes various processes in parallel or sequentially.
  • the processing device 305 reads the learning model 304a and the control program from the storage device 304, for example.
  • the processing device 305 functions as the receiving unit 310, the evaluation unit 320, and the output unit 330 by executing the learning model 304a and the control program.
  • the evaluation unit 320 is realized by executing the learning model 304a.
  • the receiving unit 310, the evaluation unit 320, and the output unit 330 are examples of functional blocks of the processing device 305. All or some of the receiving unit 310, the evaluation unit 320, and the output unit 330 may be configured by hardware such as DSP, ASIC, PLD, or FPGA.
  • the receiving unit 310 receives an evaluation reproduction log (a reproduction log indicating a status regarding reproduction of a moving image on the information processing apparatus 20) from the information processing apparatus 20.
  • an evaluation reproduction log a reproduction log indicating a status regarding reproduction of a moving image on the information processing apparatus 20
  • the learning model 304a used by the evaluation unit 320 is a learned model that has learned relational data indicating the relationship between the situation regarding the reproduction of the moving image and the evaluation regarding the situation regarding the reproduction of the moving image.
  • the learning model 304a is defined by a plurality of coefficients K.
  • the plurality of coefficients K are specified by machine learning using teacher data, which is an example of relational data.
  • the evaluation unit 320 uses the learning model 304a to evaluate the situation regarding the reproduction of the moving image performed by the reproduction software in the development stage, based on the evaluation reproduction log received by the reception unit 310.
  • the learning model 304a defines, for example, a neural network, typically a deep neural network.
  • the evaluation unit 320 is a neural network realized by the processing device (an example of a computer) 305, or more specifically, a functional block.
  • the evaluation unit 320 generates the output B according to the input A.
  • the learning model 304a is used in a program (for example, a program module that constitutes artificial intelligence software) that causes the processing device 305 to execute an operation that specifies the output A from the input A.
  • the learning model 304a includes a plurality of coefficients K applied to the calculation.
  • a predetermined response function is used for the calculation.
  • the multiple coefficients K are optimized by prior machine learning (deep learning) using relational data including multiple teacher data.
  • the input A and the output B are associated with each other. That is, the learning model 304a is a statistical model in which the relational data indicating the relation between the input A and the output B is learned.
  • the processing device 305 executes a calculation that applies a plurality of coefficients K and a predetermined response function to an unknown input A, and thereby tends to be extracted from a plurality of teacher data (between the input A and the output B).
  • Output B based on the relation (1), that is, an output B that is valid for the input A is generated.
  • a “playback log” (information indicating the status regarding playback of a moving image) is used as the input A, and an “evaluation” is used as the output B.
  • the evaluation unit 320 may be implemented by a neural network processor such as a Tensor Processing Unit (tensor processing unit) and a Neural Engine (neural engine), for example.
  • a neural network processor such as a Tensor Processing Unit (tensor processing unit) and a Neural Engine (neural engine), for example.
  • the output unit 330 outputs information based on the evaluation result by the evaluation unit 320 for the reproduction software at the development stage. For example, the output unit 330 outputs image information based on the evaluation result and audio information based on the evaluation result as the information based on the evaluation result.
  • FIG. 2 is a diagram showing an example of a learning model generation device 50 that generates a learning model 304a defined by a plurality of coefficients K.
  • elements that are the same as or similar to the elements described in FIG. 1 are assigned the same reference numerals and detailed description thereof is omitted.
  • the learning model generation device 50 generates teacher data for the learning model 304a and a plurality of coefficients K that define the learning model 304a.
  • the teacher data for the learning model 304a may be generated by a device different from the learning model generation device 50.
  • the learning model generation device 50 communicates with the information processing devices 41 to 4n (n is an integer of 1 or more) via the network NW.
  • the information processing devices 41 to 4n realize the reproduction executing unit 210 by executing the reproduction software which has been developed, not the reproduction software at the development stage, and the reproduction information for reproducing the moving image and the sound.
  • the configuration is the same as that of the information processing device 20 except that the added reproduction log is output.
  • each of the information processing devices 41 to 4n is referred to as an information processing device 4m (m is an integer of 1 to n).
  • the reproduction information is used to reproduce the moving image and sound reproduced by the information processing device 4m.
  • the reproduction information includes identification information of the distribution data used for reproducing the sound and the moving image in the information processing device 4m.
  • the reproduction information further includes, for each index number of the image frame forming the moving image, the image quality (resolution) of the image frame with the index number, whether or not the image frame is reproduced, and the audio to be synchronized with the image frame. The time difference between the reproduction timing and the image frame is shown.
  • the learning model generation device 50 is configured by a computer system including an input device 501, a display device 502, a speaker 503, a communication device 504, a storage device 505, and a processing device 506. Each element of the learning model generation device 50 is mutually connected by one or two or more buses. Further, each element of the learning model generation device 50 is configured by one or two or more devices.
  • the input device 501 is a device for inputting information used by the learning model generation device 50 to the learning model generation device 50.
  • the input device 501 receives user input such as instructions or questions from the user.
  • the input device 501 is, for example, a keyboard.
  • the input device 501 is not limited to the keyboard, and may be a touch panel, a mouse, a switch, a button, or the like that detects contact with the display surface of the display device 502.
  • the display device 502 displays various images under the control of the processing device 506. For example, the display device 502 displays a moving image based on the moving image data included in the distribution data.
  • the display device 502 is, for example, a liquid crystal display panel or an organic EL display panel.
  • the speaker 503 outputs various sounds by being controlled by the processing device 506. For example, the speaker 503 outputs a sound based on the sound data included in the distribution data.
  • the communication device 504 communicates with other devices, for example, the information processing devices 41 to 4n, via the network NW.
  • the communication device 504 is also called, for example, a network device, a network controller, a network card, or a communication module.
  • the storage device 505 is a recording medium that can be read by the processing device 506.
  • the storage device 505 stores a control program executed by the processing device 506 and various data used by the processing device 506.
  • the storage device 505 is composed of, for example, a non-volatile memory (for example, ROM, EPROM, and EEPROM) and a volatile memory (for example, RAM).
  • the processing device 506 is a processor that controls the learning model generation device 50.
  • the processing device 506 includes, for example, one or more chips.
  • the processing device 506 is configured by a central processing unit (CPU).
  • CPU central processing unit
  • Some or all of the functions of the processing device 506 may be realized by hardware such as DSP, ASIC, PLD, or FPGA.
  • the processing device 506 executes various processes in parallel or sequentially.
  • the processing device 506 reads the control program from the storage device 505.
  • the processing device 506 functions as the receiving unit 510, the reproducing unit 520, the evaluation obtaining unit 530, the teacher data generating unit 540, and the learning model generating unit 550 by executing the control program.
  • the reception unit 510, the reproduction unit 520, the evaluation acquisition unit 530, the teacher data generation unit 540, and the learning model generation unit 550 are examples of functional blocks of the processing device 506. All or some of the receiving unit 510, the reproducing unit 520, the evaluation obtaining unit 530, the teacher data generating unit 540, and the learning model generating unit 550 may be configured by hardware such as DSP, ASIC, PLD, or FPGA.
  • the receiving unit 510 receives the reproduction log from the information processing device 4m via the communication device 504.
  • the reproduction log received from the information processing device 4m will be referred to as a "learning reproduction log”.
  • the reproduction log for learning shows the number of consecutive frames dropped and the time difference between the reproduction timings of the audio and the moving image. Replay information is added to the learning log for learning.
  • the reproduction unit 520 reproduces the reproduction status of the moving image and the audio in the information processing device 4m, using the display device 502 and the speaker 503, based on the reproduction information for each reproduction reproduction log for learning.
  • the evaluator who evaluates the reproduction quality of the moving image evaluates the quality of the reproduction of the moving image reproduced using the display device 502 and the speaker 503.
  • the evaluator inputs the evaluation result of the reproduction quality of the moving image into the input device 501.
  • the evaluation acquisition unit 530 receives, from the input device 501, the evaluation result regarding the reproduction quality of the moving image for each learning reproduction log.
  • the teacher data generation unit 540 generates teacher data for each learning reproduction log.
  • Each teacher data is a set of a learning reproduction log and a quality evaluation result (label) regarding reproduction of a moving image reproduced based on the learning reproduction log.
  • the learning model generation unit 550 generates a plurality of coefficients K defining the learning model 304a by performing machine learning (deep learning) using the teacher data generated by the teacher data generation unit 540.
  • FIG. 3 is a flowchart for explaining an example of the operation of the learning model generation device 50.
  • reference to the communication devices 204 and 504 will be omitted in the communication operation between the learning model generation device 50 and the information processing device 4m.
  • the receiving unit 510 receives the learning reproduction log from the information processing device 4m (step S100).
  • the receiving unit 510 receives a learning reproduction log that the information processing device 4m voluntarily transmits.
  • the receiving unit 510 may transmit a request for a learning reproduction log to the information processing apparatus 4m, and may receive a learning reproduction log transmitted by the information processing apparatus 4m in response to the learning reproduction log request.
  • the reproduction unit 520 reproduces the reproduction status of the moving image and the audio in the information processing device 4m based on the reproduction information for each learning reproduction log (step S102).
  • the reproduction unit 520 first obtains from the distribution server 10 the distribution data specified by the identification information of the distribution data indicated by the reproduction information. Specifically, the reproduction unit 520 acquires, from the distribution server 10, distribution data indicating a moving image having the highest image quality among the image quality indicated for each index number of the image frame in the reproduction information. The reproduction unit 520 may obtain the distribution data from a server different from the distribution server 10. Subsequently, the reproduction unit 520 reproduces the reproduction state of the moving image and the audio indicated by the reproduction information using the display device 502 and the speaker 503 based on the distribution data and the reproduction information.
  • the image quality of each image frame, the presence or absence of reproduction of each image frame, and the time difference between the reproduction timing of the sound of each image frame and the sound are reproduced. Since the distribution data indicating the moving image having the highest image quality among the image quality indicated for each image frame index number is used, the conversion to any image quality indicated for each image frame index number is possible.
  • the evaluator evaluates the reproduction quality of the moving image reproduced using the display device 502 and the speaker 503, and inputs the evaluation result to the input device 501.
  • the evaluation acquisition unit 530 receives the evaluation result input to the input device 501 (step S104).
  • the teacher data generation unit 540 deletes the reproduction information from the reproduction reproduction log for learning.
  • the teacher data generation unit 540 evaluates the learning reproduction log for each learning reproduction log in which the reproduction information is deleted, and the reproduction quality of the moving image reproduced based on the learning reproduction log.
  • a pair of the result and is generated as teacher data step S106. Therefore, the teacher data indicates the relationship between the situation regarding the reproduction of the moving image (the situation indicated by the reproduction log) and the evaluation regarding the situation regarding the reproduction of the moving image (evaluation result).
  • the learning model generation unit 550 performs machine learning (deep learning) using the teacher data generated by the teacher data generation unit 540 (step S108), and thereby a plurality of coefficients K defining the learning model 304a. Is specified (step S110).
  • the plurality of coefficients K specified by the learning model generation unit 550 are stored in the storage device 304 shown in FIG. 1 by the evaluator, for example.
  • the evaluation device 30 implements the evaluation unit 320 based on the plurality of coefficients K stored in the storage device 304.
  • the learning model 304a learns the relationship between the situation regarding the reproduction of the moving image and the evaluation regarding the situation regarding the reproduction of the moving image.
  • FIG. 4 is a flowchart for explaining an example of the operation of the evaluation device 30.
  • the reference to the communication devices 204 and 301 in the communication between the evaluation device 30 and the information processing device 20 will be omitted.
  • the receiving unit 310 receives the reproduction log for evaluation from the information processing device 20 (step S200).
  • step S200 the receiving unit 310 receives the reproduction log for evaluation that the information processing apparatus 20 voluntarily transmits.
  • the receiving unit 310 may send a request for a reproduction log for evaluation to the information processing apparatus 20, and receive the reproduction log for evaluation transmitted by the information processing apparatus 20 in response to the request for the reproduction log for evaluation.
  • the evaluation unit 320 uses the learning model 304a to evaluate the situation regarding the reproduction of the moving image performed by the reproduction software in the development stage based on the reproduction log received by the reception unit 310 (step S202).
  • step S202 the evaluation unit 320 receives the evaluation reproduction log as an input, and outputs the evaluation of the situation regarding the reproduction of the moving image performed by the reproduction software in the development stage.
  • the receiving unit 310 may input the reproduction log for evaluation to the evaluation unit 320.
  • the output unit 330 outputs information based on the result of the evaluation performed by the evaluation unit 320 on the reproduction software at the development stage (step S204).
  • step S204 the output unit 330 generates evaluation image information indicating the result of the evaluation performed by the evaluation unit 320, and outputs the evaluation image information to the display device 302.
  • the display device 302 Upon receiving the evaluation image information, the display device 302 displays the image indicated by the evaluation image information (the image indicating the result of the evaluation performed by the evaluation unit 320).
  • the output unit 330 indicates by the first image that the reproduction software performed in the development stage has poor reproduction quality.
  • the first evaluation image information is output to the display device 302.
  • the first image represents, for example, a character "reproduction quality is poor.” Note that the first image is not limited to the image representing the character "reproduction quality is poor.”, but may be an image representing the symbol "x", for example, and can be appropriately changed.
  • the display device 302 Upon receiving the first evaluation image information, the display device 302 displays the first image.
  • the output unit 330 uses the second evaluation image that indicates that the quality of reproduction performed by the reproduction software in the development stage is good by the second image.
  • the information is output to the display device 302.
  • the second image represents, for example, a character "The reproduction quality is good.” It should be noted that the second image is not limited to the image showing the character “The reproduction quality is good.”, but may be an image showing the symbol “ ⁇ ”, for example, and can be changed appropriately.
  • the display device 302 displays the second image.
  • step S204 the output unit 330 generates evaluation voice information based on the result of the evaluation performed by the evaluation unit 320, and outputs the evaluation voice information to the speaker 303.
  • the speaker 303 Upon receiving the evaluation voice information, the speaker 303 outputs a sound according to the evaluation voice information.
  • the output unit 330 expresses by the first voice that the reproduction quality performed by the reproduction software in the development stage is poor.
  • the first evaluation voice information to be output is output to the speaker 303.
  • the first voice is, for example, a voice "reproduction quality is poor.”
  • the first voice is not limited to the voice "reproduction quality is poor.”
  • the speaker 303 Upon receiving the first evaluation voice information, the speaker 303 outputs the first voice according to the first evaluation voice information.
  • the output unit 330 expresses by the second voice that the reproduction quality performed by the reproduction software in the development stage is good.
  • the evaluation voice information is output to the speaker 303.
  • the second sound is, for example, a sound "the reproduction quality is good.”
  • the second voice is not limited to the voice "The reproduction quality is good", but may be the voice "OK", for example, and can be changed as appropriate.
  • the speaker 303 Upon receiving the second evaluation audio information, the speaker 303 outputs the second audio corresponding to the second evaluation audio information.
  • the output unit 330 may output an electronic file indicating the evaluation result to another device as information based on the result of the evaluation performed by the evaluation unit 320.
  • the electronic file showing the evaluation result for example, an electronic file of text data showing the evaluation result and an electronic file for spreadsheet showing the evaluation result are used.
  • the evaluation unit 320 uses the learning model 304a that learned the relational data indicating the relationship between the situation regarding the reproduction of the moving image and the evaluation for the situation, and the reproduction situation regarding the reproduction of the moving image performed by the reproduction software. Evaluate. For this reason, compared to a case where a person evaluates the reproduction state of a moving image performed by the reproduction software in the development stage, the labor of the test in the development stage of the reproduction software can be reduced.
  • the receiving unit 310 receives a playback log for evaluation indicating the status regarding playback of a moving image performed by the playback software, and the evaluation unit 320 displays the evaluation result according to the playback status indicated by the playback log for evaluation received by the receiving unit 310. To generate. Therefore, there is an advantage that the evaluation device 30 does not need to generate the reproduction log for evaluation.
  • the status regarding the playback of the moving picture includes both the status of the number of unplayed image frames and the status of the timing difference between the playback timing of the audio and the moving picture.
  • the higher the number of non-reproduced image frames the worse the reproduction quality of the moving image.
  • the longer the time difference between the playback timings of audio and video the worse the quality of video playback.
  • a learning model is used that learns the relationship between the situation of the number of unreproduced image frames and the situation of the reproduction of the video that does not include the situation of the reproduction timing of the audio and the video, and the evaluation thereof.
  • the reliability of the evaluation performed by the evaluation unit 320 can be increased as compared with the evaluation.
  • the status related to video playback includes one of the status of the number of non-playback image frames and the status of timing difference between audio and video playback, status related to video playback that does not include either
  • the reliability of the evaluation performed by the evaluation unit 320 can be made higher than the evaluation using the learning model in which the relationship between the evaluation and the evaluation is learned.
  • the status of the number of non-playback image frames indicates the number of consecutive non-playback image frames.
  • the situation regarding the reproduction of the moving image indicated by the reproduction log may further include the state of the load of the device that is reproducing the moving image.
  • the load status of a device playing a moving image (hereinafter referred to as “playback device”) is also referred to as “device load status”.
  • the load status of the device indicates the decoding time required for the playback device to decode the distribution data (hereinafter, simply referred to as “decoding time”). Generally, since the decoding time of moving image data is longer than the decoding time of audio data, the time required for decoding moving image data is used as the decoding time included in the load status of the device.
  • the device load status further indicates a decryption time (hereinafter, referred to as “decrypt time”) required for decryption, which is a reproduction device decrypting distribution data encryption (for example, encryption using DRM).
  • decrypt time a decryption time required for decryption
  • the encryption of distribution data is not limited to the encryption using DRM.
  • the decryption time of moving image data is longer than the decryption time of audio data, the decryption time of moving image data is used as the decryption time included in the load status of the device.
  • the load status of the device may include only one of the decoding time and the decryption time, not both.
  • the status regarding the playback of the video indicated by the playback log may further indicate the bit rate of the video data.
  • each of the learning reproduction log and the evaluation reproduction log includes the number of consecutive frames dropped, the time difference between the reproduction timings of audio and video, the bit rate of video data, and the decoding time. And the decryption time.
  • the longer the decoding time the more likely the number of consecutive frames dropped.
  • the longer the decoding time the longer the time lag between the audio and video playback timings tends to be. Therefore, the longer the decoding time, the worse the reproduction quality of the moving image.
  • the longer the decryption time the more likely the number of consecutive frames dropped.
  • the longer the decryption time the longer the time difference between the audio and video playback timing. Therefore, the longer the decryption time, the worse the reproduction quality of the moving image.
  • the number of consecutive dropped frames, the time difference between audio and video playback timing, the video data bit rate, the decoding time, and the decryption time affect the video playback quality.
  • the learning model 304a has a relationship between the evaluation and the reproduction log indicating the number of consecutive frames dropped, the time difference between the audio and video reproduction timing, the video data bit rate, the decoding time, and the decryption time.
  • the learning model 304a of the first embodiment learns relational data indicating the relation between the evaluation and the reproduction log indicating only the number of continuous frame dropouts and/or the time difference between the reproduction timings of audio and moving images. .. Therefore, the result of the evaluation performed by the evaluation unit 320 in the first modification has higher reliability than the result of the evaluation performed by the evaluation unit 320 in the first embodiment.
  • the playback log for learning and the playback log for evaluation show only the number of consecutive frames dropped, the time difference between the playback timing of audio and video, the bit rate of video data, the decoding time, and the decryption time, but not all. May be.
  • the playback log for learning and the playback log for evaluation include a decoding time and a bit rate of video data in addition to one or both of the number of consecutive dropped frames and the time difference between the audio and video playback timings.
  • Decryption time only one or two may be shown.
  • the playback log for learning and the playback log for evaluation include the decoding time and the bit rate of the video data in addition to the number of consecutive frames dropped and the time difference between the audio and video playback timing. , And the decryption time may all be shown.
  • the learning model that learned the relationship between the situation of the number of unreproduced image frames and the situation of the reproduction of the video that does not include the situation of the timing difference between the reproduction timing of the audio and the video and the evaluation thereof.
  • the reliability of the evaluation performed by the evaluation unit 320 can be increased as compared with the evaluation using.
  • the evaluation device 30 illustrated in FIG. 1 may include the function of the learning model generation device 50 illustrated in FIG. 2.
  • FIG. 5 is a diagram showing an example of the evaluation device 30 including the function of the learning model generation device 50. 5, elements that are the same as or similar to the elements described in FIG. 1 or FIG. 2 are given the same reference numerals, and detailed description thereof will be omitted.
  • the receiving unit 310 also serves as the receiving unit 510.
  • the communication device 301 also serves as the communication device 504.
  • the display device 302 also serves as the display device 502.
  • the speaker 303 also serves as the speaker 503.
  • the storage device 304 also serves as the storage device 505.
  • the processing device 305 also serves as the processing device 506.
  • the processing device 305 reads the control program and the learning model 304a stored in the storage device 304.
  • the processing device 305 executes the control program and the learning model 304a to obtain the receiving unit 310, the evaluation unit 320, the output unit 330, the reproduction unit 520, the evaluation acquisition unit 530, the teacher data generation unit 540, and the learning model generation unit 550. Function as.
  • the learning model generation unit 550 can store the plurality of coefficients K specified by the learning model generation unit 550 in the storage device 304. Therefore, a person (for example, an evaluator) can save the trouble of storing the plurality of coefficients K specified by the learning model generation unit 550 in the storage device 304.
  • the evaluation device 30 may further include a vibration device.
  • the output unit 330 may vibrate the vibrating device using the vibration information based on the evaluation result performed by the evaluation unit 320. For example, when the result of the evaluation performed by the evaluation unit 320 indicates that the reproduction quality is poor, the output unit 330 outputs the vibration information that causes the vibration device to vibrate, to the vibration device. On the other hand, when the result of the evaluation performed by the evaluation unit 320 indicates that the quality of reproduction is good, the output unit 330 does not output the vibration information for vibrating the vibration device to the vibration device.
  • first storage device that stores the reproduction log for evaluation in the information processing device 20
  • the reproduction log for evaluation of the information processing device 20 may be received from the first storage device.
  • the receiving unit 310 receives the reproduction log for evaluation that the first storage device voluntarily transmits.
  • the receiving unit 310 may send a request for a playback log for evaluation to the first storage device, and may receive a playback log for evaluation sent by the first storage device in response to the request for a playback log for evaluation. ..
  • the receiving unit 510 when there is a device (hereinafter, referred to as “second storage device”) that stores the reproduction log for learning in the information processing device 4m, the receiving unit 510 The reproduction log for learning of the information processing device 4m may be received from the second storage device.
  • the receiving unit 310 that also serves as the receiving unit 510 may receive the learning reproduction log of the information processing device 4m from the second storage device.
  • the receiving unit 510 receives the learning reproduction log voluntarily transmitted by the second storage device.
  • the receiving unit 310 may transmit a request for a learning reproduction log to the second storage device, and may receive a learning reproduction log that the second storage device transmits in response to the request for the learning reproduction log. ..
  • the evaluation device 30 may be installed with reproduction software at the development stage.
  • the processing device 305 may read the reproduction software at the development stage from the evaluation device 30.
  • the processing device 305 may further function as the reproduction executing unit 210 by executing the reproduction software at the development stage read from the evaluation device 30. In this case, since the evaluation device 30 can generate the reproduction log for evaluation, the information processing device 20 can be omitted.
  • the learning model 304a may be represented by, for example, an SVM (Support Vector Machine) or an HMM (Hidden Markov Model).
  • the storage devices 102, 205, 304, and 505 are flexible disks, magneto-optical disks (for example, compact disks, digital versatile disks, Blu-ray (registered trademark) disk, smart card, flash memory device (for example, card, stick, key drive), CD-ROM (Compact Disc-ROM), register, removable disk, hard disk, floppy (registered trademark) disk , Magnetic strips, databases, servers and other suitable storage media.
  • the program may be transmitted from the network via an electric communication line.
  • the first embodiment and each of the first to seventh modifications are LTE (Long Term Evolution), LTE-A (LTE-Advanced), SUPER 3G, IMT-Advanced, 4G, 5G, and FRA ( Future Radio Access), W-CDMA (registered trademark), GSM (registered trademark), CDMA2000, UMB (Ultra Mobile Broadband), IEEE 802.11 (Wi-Fi), IEEE 802.16 (WiMAX), IEEE 802.20. , UWB (Ultra-WideBand), Bluetooth (registered trademark), and other suitable systems, and/or may be applied to a next-generation system extended based on these systems.
  • each of the first embodiment and each of the first modification to the seventh modification may be represented using any of various different technologies.
  • data, information, bits, chips, etc. that may be referred to throughout the above description may be represented by voltage, current, electromagnetic waves, magnetic fields, magnetic particles, optical fields, photons, or any combination thereof. Good. Note that the terms described in the present specification and/or the terms necessary for understanding the present specification may be replaced with terms having the same or similar meanings.
  • the input/output information and the like may be stored in a specific location (for example, a memory) or may be managed by a management table. Good. Information that is input/output may be overwritten, updated, or added. The output information and the like may be deleted. The input information and the like may be transmitted to another device.
  • the determination may be made based on the value (0 or 1) represented by 1 bit, or the true/false value. It may be performed based on (Boolean: true or false) or may be performed based on comparison of numerical values (for example, comparison with a predetermined value).
  • Each function illustrated in FIG. 1, FIG. 2 or FIG. 5 is realized by an arbitrary combination of hardware and software. Further, each function may be realized by a single device, or may be realized by two or more devices that are configured separately from each other.
  • the software may use a wired technology such as coaxial cable, fiber optic cable, twisted pair and digital subscriber line (DSL) and/or wireless technology such as infrared, wireless and microwave to websites, servers, or other When transmitted from a remote source, these wireline and/or wireless technologies are included within the definition of transmission medium.
  • a wired technology such as coaxial cable, fiber optic cable, twisted pair and digital subscriber line (DSL) and/or wireless technology such as infrared, wireless and microwave to websites, servers, or other
  • DSL digital subscriber line
  • wireless technology such as infrared, wireless and microwave
  • the information processing devices 20 and 41 to 4n may be mobile stations.
  • Mobile stations are defined by those skilled in the art as subscriber stations, mobile units, subscriber units, wireless units, remote units, mobile devices, wireless devices, wireless communication devices, remote devices, mobile subscriber stations, access terminals, mobile terminals, wireless. It may also be referred to as a terminal, remote terminal, handset, user agent, mobile client, client, or some other suitable term.

Abstract

This evaluation device includes: an evaluation unit that evaluates a reproduction state related to video reproduction carried out by video-reproduction software, such evaluation using a learning model in which learned is relationship data indicating the relationship between a state related to video reproduction and an evaluation of such state; and an output unit that outputs information based on the evaluation results from the evaluation unit.

Description

評価装置Evaluation device
　本発明は、評価装置に関する。 The present invention relates to an evaluation device.
　特許文献１には、配信サイトが送信する動画データに基づいて動画を再生するユーザ端末が記載されている。特許文献１に記載されたようなユーザ端末は、動画データに基づいて動画を再生するためのソフトウェア（以下「動画再生用ソフトウェア」と称する）を実行することによって動画を再生する。動画再生用ソフトウェアは、開発段階でのテストにパスした後に、ユーザ端末に提供される。 Patent Document 1 describes a user terminal that reproduces a moving image based on moving image data transmitted by a distribution site. A user terminal as described in Patent Document 1 reproduces a moving image by executing software for reproducing the moving image based on the moving image data (hereinafter referred to as “moving image reproducing software”). The video playback software is provided to the user terminal after passing the test in the development stage.
特開２０１８－１３３７６７号公報JP, 2008-133767, A
　動画再生用ソフトウェアの開発段階でのテストにおいては、人が、開発段階の動画再生用ソフトウェアが行う動画の再生の状況を評価する。このため、動画再生用ソフトウェアの開発段階でのテストは手間を要する。 In a test of the video playback software development stage, a person evaluates the status of video playback performed by the video playback software in the development stage. For this reason, testing at the development stage of the video playback software requires time and effort.
　本発明の目的は、動画再生用ソフトウェアの開発段階でのテストの手間を低減できる技術を提供することである。 An object of the present invention is to provide a technology that can reduce the time and effort required for testing the moving image playback software during the development stage.
　本発明の一態様に係る評価装置は、動画の再生に関する状況と、前記状況に対する評価と、の関係を示す関係データを学習した学習モデルを用いて、動画再生用ソフトウェアが行う動画の再生に関する再生状況を評価する評価部と、前記評価部での評価結果に基づく情報を出力する出力部と、を含む。 An evaluation device according to one aspect of the present invention uses a learning model that has learned relational data indicating a relationship between a situation regarding reproduction of a moving image and an evaluation for the situation, and performs reproduction regarding reproduction of a moving image performed by software for reproducing a moving image. An evaluation unit that evaluates the situation and an output unit that outputs information based on the evaluation result of the evaluation unit are included.
　本発明の一態様によれば、動画再生用ソフトウェアの開発段階でのテストの手間を低減できる。 According to one aspect of the present invention, it is possible to reduce the time and effort required for testing in the development stage of video playback software.
第１実施形態に係る評価システム１の全体構成を示す図である。It is a figure which shows the whole structure of the evaluation system 1 which concerns on 1st Embodiment. 学習モデル生成装置５０の一例を示す図である。It is a figure which shows an example of the learning model generation apparatus 50. 学習モデル生成装置５０の動作の一例を示すフローチャートである。7 is a flowchart showing an example of the operation of the learning model generation device 50. 評価装置３０の動作の一例を示すフローチャートである。6 is a flowchart showing an example of the operation of the evaluation device 30. 学習モデル生成装置５０の機能を含む評価装置３０の一例を示す図である。It is a figure which shows an example of the evaluation apparatus 30 containing the function of the learning model generation apparatus 50.
＜Ａ．第１実施形態＞
　図１は、第１実施形態に係る評価システム１の全体構成を示す図である。評価システム１は、動画と音声とを再生する再生用ソフトウェアの開発段階において、再生用ソフトウェアを評価する。再生用ソフトウェアは、動画再生用ソフトウェアの一例である。 <A. First Embodiment>
FIG. 1 is a diagram showing an overall configuration of an evaluation system 1 according to the first embodiment. The evaluation system 1 evaluates the reproduction software at the development stage of the reproduction software for reproducing the moving image and the sound. The playback software is an example of moving image playback software.
＜Ａ１．評価システム１＞
　図１に例示されるように、評価システム１は、配信サーバ１０、情報処理装置２０及び評価装置３０を含む。配信サーバ１０、情報処理装置２０及び評価装置３０は、ネットワークＮＷを介して相互に通信可能である。なお、評価システム１において、配信サーバ１０、情報処理装置２０及び評価装置３０の各々の数は「１」に限定されない。例えば、評価システム１は、複数の配信サーバ１０、複数の情報処理装置２０及び複数の評価装置３０を含んでもよい。 <A1. Evaluation system 1>
As illustrated in FIG. 1, the evaluation system 1 includes a distribution server 10, an information processing device 20, and an evaluation device 30. The distribution server 10, the information processing device 20, and the evaluation device 30 can communicate with each other via the network NW. In the evaluation system 1, the number of each of the distribution server 10, the information processing device 20, and the evaluation device 30 is not limited to “1”. For example, the evaluation system 1 may include a plurality of distribution servers 10, a plurality of information processing devices 20, and a plurality of evaluation devices 30.
＜Ａ２．配信サーバ１０＞
　配信サーバ１０は、動画を示す動画データと音声を示す音声データとを含む配信データを、ユーザ装置２０に送信する。配信データは、例えば、映画を表すデータである。配信データは、映画を表すデータに限らず、例えば、テレビ番組を表すデータでもよい。動画データと音声データの各々は、エンコードされたデータである。配信データにおいて、動画データに基づく動画の再生タイミングと、音声データに基づく音声の再生タイミングとは、予め規定されている。動画データの示す動画のフレームレートは、基準フレームレートに固定されている。基準フレームレートは、例えば、３０ｆｐｓ（Frames Per Second）である。基準フレームレートは、３０ｆｐｓに限らず３０ｆｐｓよりも高くてもよく低くてもよい。なお、動画データの示す動画のフレームレートは変更されてもよい。配信サーバ１０は、ユーザ装置２０からの要求に応じて配信データのビットレートを調整する。 <A2. Distribution server 10>
The distribution server 10 transmits distribution data including moving image data indicating a moving image and sound data indicating sound to the user device 20. The distribution data is, for example, data representing a movie. The distribution data is not limited to data representing a movie, but may be data representing a television program, for example. Each of the moving image data and the audio data is encoded data. In the distribution data, the reproduction timing of the moving image based on the moving image data and the reproduction timing of the sound based on the audio data are defined in advance. The frame rate of the moving image indicated by the moving image data is fixed to the reference frame rate. The reference frame rate is, for example, 30 fps (Frames Per Second). The reference frame rate is not limited to 30 fps and may be higher or lower than 30 fps. The frame rate of the moving image indicated by the moving image data may be changed. The distribution server 10 adjusts the bit rate of distribution data in response to a request from the user device 20.
　配信サーバ１０は、通信装置１０１、記憶装置１０２及び処理装置１０３を含むコンピュータシステムによって構成される。本明細書において、通信装置、記憶装置、処理装置、後述する入力装置、及び、後述する表示装置における「装置」という用語は、回路、デバイス又はユニット等の他の用語に読み替えられてもよい。配信サーバ１０の各要素は、単体又は複数のバスによって相互に接続される。また、配信サーバ１０の各要素は、１又は２以上の機器によって構成される。 The distribution server 10 is composed of a computer system including a communication device 101, a storage device 102, and a processing device 103. In this specification, the term “device” in a communication device, a storage device, a processing device, an input device described below, and a display device described below may be replaced with another term such as a circuit, a device, or a unit. The respective elements of the distribution server 10 are mutually connected by a single bus or a plurality of buses. Each element of the distribution server 10 is composed of one or more devices.
　通信装置１０１は、ネットワークＮＷを介して他の装置、例えば、情報処理装置２０と通信する。通信装置１０１は、例えば、ネットワークデバイス、ネットワークコントローラ、ネットワークカード又は通信モジュールとも称される。 The communication device 101 communicates with another device, for example, the information processing device 20, via the network NW. The communication device 101 is also called, for example, a network device, a network controller, a network card, or a communication module.
　記憶装置１０２は、処理装置１０３が読み取り可能な記録媒体である。記憶装置１０２は、処理装置１０３によって実行される制御プログラムを含む複数のプログラム、処理装置１０３によって使用される各種のデータ、及び、種々の配信データを記憶する。記憶装置１０２は、例えば、ＲＯＭ（Read Only Memory）、ＥＰＲＯＭ（Erasable Programmable ＲＯＭ）、ＥＥＰＲＯＭ（Electrically Erasable Programmable ＲＯＭ）及びＲＡＭ（Random Access Memory）等の記録媒体の少なくとも１つによって構成される。 The storage device 102 is a recording medium that can be read by the processing device 103. The storage device 102 stores a plurality of programs including a control program executed by the processing device 103, various data used by the processing device 103, and various distribution data. The storage device 102 is configured by at least one of a recording medium such as a ROM (Read Only Memory), an EPROM (Erasable Programmable ROM), an EEPROM (Electrically Erasable Programmable ROM), and a RAM (Random Access Memory).
　処理装置１０３は、配信サーバ１０を制御するプロセッサである。処理装置１０３は、例えば、１又は２以上のチップによって構成される。一例を挙げると、処理装置１０３は、周辺装置とのインタフェースと、中央処理装置（ＣＰＵ：Central Processing Unit）とによって構成される。中央処理装置は、演算装置及びレジスタ等を含む。処理装置１０３の機能の一部又は全部は、ＤＳＰ（Digital Signal Processor）、ＡＳＩＣ（Application Specific Integrated Circuit）、ＰＬＤ（Programmable Logic Device）、ＦＰＧＡ（Field Programmable Gate Array）等のハードウェアによって実行されてもよい。処理装置１０３は、各種の処理を並列的又は逐次的に実行する。 The processing device 103 is a processor that controls the distribution server 10. The processing device 103 is composed of, for example, one or more chips. As an example, the processing device 103 includes an interface with peripheral devices and a central processing unit (CPU). The central processing unit includes an arithmetic unit, a register and the like. Even if some or all of the functions of the processing device 103 are executed by hardware such as DSP (Digital Signal Processor), ASIC (Application Specific Integrated Circuit), PLD (Programmable Logic Device), FPGA (Field Programmable Gate Array), etc. Good. The processing device 103 executes various processes in parallel or sequentially.
　処理装置１０３は、記憶装置１０２から制御プログラムを読み取る。処理装置１０３は、当該制御プログラムを実行することによって配信制御部１１０として機能する。 The processing device 103 reads the control program from the storage device 102. The processing device 103 functions as the distribution control unit 110 by executing the control program.
　配信制御部１１０は、処理装置１０３の機能ブロックの一例である。配信制御部１１０は、ＤＳＰ、ＡＳＩＣ、ＰＬＤ又はＦＰＧＡ等のハードウェアによって構成されてもよい。配信制御部１１０は、配信データの送信を制御する。例えば、配信制御部１１０は、情報処理装置２０の要求に応じて、情報処理装置２０に配信データを送信する。また、配信制御部１１０は、情報処理装置２０の要求に応じて、配信データに含まれる動画データのビットレートを調整する。動画データのビットレートは、１秒当たりに送信される動画データのデータ量を表す。１秒は、単位時間の一例である。単位時間は、１秒に限らず、１秒よりも長くてもよいし短くてもよい。 The delivery control unit 110 is an example of a functional block of the processing device 103. The distribution control unit 110 may be configured by hardware such as DSP, ASIC, PLD, or FPGA. The delivery control unit 110 controls transmission of delivery data. For example, the distribution control unit 110 transmits distribution data to the information processing device 20 in response to a request from the information processing device 20. The distribution control unit 110 also adjusts the bit rate of the moving image data included in the distribution data in response to a request from the information processing device 20. The bit rate of moving image data represents the amount of moving image data transmitted per second. One second is an example of unit time. The unit time is not limited to 1 second and may be longer or shorter than 1 second.
＜Ａ３．情報処理装置２０＞
　情報処理装置２０は、例えば、スマートフォンである。なお、情報処理装置２０は、スマートフォンに限定されない。例えば、情報処理装置２０は、ノートパソコン、ウェアラブル端末、タブレット端末又はデスクトップパソコン等でもよい。情報処理装置２０は、配信サーバ１０から配信データを受信する。情報処理装置２０には、開発段階の再生用ソフトウェアがインストールされている。情報処理装置２０は、開発段階の再生用ソフトウェアを用いて配信データを処理することによって動画及び音声を再生する。 <A3. Information processing device 20>
The information processing device 20 is, for example, a smartphone. The information processing device 20 is not limited to the smartphone. For example, the information processing device 20 may be a notebook computer, a wearable terminal, a tablet terminal, a desktop computer, or the like. The information processing device 20 receives the distribution data from the distribution server 10. Playback software at the development stage is installed in the information processing device 20. The information processing device 20 reproduces the moving image and the sound by processing the distribution data using the reproduction software in the development stage.
　情報処理装置２０は、入力装置２０１、表示装置２０２、スピーカ２０３、通信装置２０４、記憶装置２０５及び処理装置２０６を含むコンピュータシステムによって構成される。情報処理装置２０の各要素は、１又は２以上のバスによって相互に接続される。また、情報処理装置２０の各要素は、１又は２以上の機器によって構成される。 The information processing device 20 is configured by a computer system including an input device 201, a display device 202, a speaker 203, a communication device 204, a storage device 205, and a processing device 206. Each element of the information processing device 20 is mutually connected by one or more buses. Further, each element of the information processing device 20 is configured by one or more devices.
　入力装置２０１は、情報処理装置２０によって使用される情報を情報処理装置２０に入力するための機器である。入力装置２０１は、ユーザからの指示又は質問等のユーザの入力を受け取る。例えば、入力装置２０１は、数字及び文字等の符号を情報処理装置２０に入力するための操作と、表示装置２０２が表示するアイコンを選択するための操作と、を受け取る。入力装置２０１は、例えば、表示装置２０２の表示面に対する接触を検出するタッチパネルである。なお、入力装置２０１は、タッチパネルに限定されない。例えば、入力装置２０１は、キーボード、マウス、スイッチ又はボタン等でもよい。 The input device 201 is a device for inputting information used by the information processing device 20 to the information processing device 20. The input device 201 receives user input such as instructions or questions from the user. For example, the input device 201 receives an operation for inputting codes such as numbers and letters to the information processing device 20 and an operation for selecting an icon displayed by the display device 202. The input device 201 is, for example, a touch panel that detects contact with the display surface of the display device 202. The input device 201 is not limited to the touch panel. For example, the input device 201 may be a keyboard, a mouse, a switch, a button, or the like.
　表示装置２０２は、処理装置２０６によって制御されることによって各種の画像を表示する。例えば、表示装置２０２は、アイコンと、配信データに含まれる動画データに基づく動画とを、同時に又は互いに異なるタイミングにおいて表示する。表示装置２０２は、例えば、液晶表示パネルである。なお、表示装置２０２は、液晶表示パネルに限定されない。例えば、表示装置２０２は、有機ＥＬ（Electro Luminescence）表示パネル等でもよい。 The display device 202 displays various images under the control of the processing device 206. For example, the display device 202 displays the icon and the moving image based on the moving image data included in the distribution data at the same time or at different timings. The display device 202 is, for example, a liquid crystal display panel. The display device 202 is not limited to the liquid crystal display panel. For example, the display device 202 may be an organic EL (Electro Luminescence) display panel or the like.
　スピーカ２０３は、処理装置２０６によって制御されることによって各種の音を出力する。例えば、スピーカ２０３は、配信データに含まれる音声データに基づく音声を出力する。 The speaker 203 outputs various sounds under the control of the processing device 206. For example, the speaker 203 outputs a sound based on the sound data included in the distribution data.
　通信装置２０４は、ネットワークＮＷを介して他の装置、例えば、配信サーバ１０及び評価装置３０の各々と通信する。通信装置２０４は、例えば、ネットワークデバイス、ネットワークコントローラ、ネットワークカード又は通信モジュールとも称される。 The communication device 204 communicates with other devices, for example, the distribution server 10 and the evaluation device 30 via the network NW. The communication device 204 is also called, for example, a network device, a network controller, a network card, or a communication module.
　記憶装置２０５は、処理装置２０６が読み取り可能な記録媒体である。記憶装置２０５は、処理装置２０６によって実行される複数のプログラム、及び、処理装置２０６が使用する各種のデータを記憶する。処理装置２０６によって実行される複数のプログラムには、開発段階の再生用ソフトウェアが含まれる。記憶装置２０５は、例えば、不揮発性メモリと揮発性メモリとによって構成される。不揮発性メモリは、例えば、ＲＯＭ、ＥＰＲＯＭ又はＥＥＰＲＯＭである。揮発性メモリは、例えば、ＲＡＭである。記憶装置２０５に含まれる揮発性メモリは、処理装置２０６の作業用メモリとして用いられる。 The storage device 205 is a recording medium that can be read by the processing device 206. The storage device 205 stores a plurality of programs executed by the processing device 206 and various data used by the processing device 206. The plurality of programs executed by the processing device 206 include reproduction software at the development stage. The storage device 205 includes, for example, a non-volatile memory and a volatile memory. The non-volatile memory is, for example, ROM, EPROM, or EEPROM. The volatile memory is, for example, a RAM. The volatile memory included in the storage device 205 is used as a working memory of the processing device 206.
　処理装置２０６は、情報処理装置２０を制御するプロセッサである。処理装置２０６は、例えば、１又は２以上のチップによって構成される。一例を挙げると、処理装置２０６は、中央処理装置（ＣＰＵ）によって構成される。処理装置２０６の機能の一部又は全部は、ＤＳＰ、ＡＳＩＣ、ＰＬＤ又はＦＰＧＡ等のハードウェアによって実現されてもよい。処理装置２０６は、各種の処理を並列的又は逐次的に実行する。 The processing device 206 is a processor that controls the information processing device 20. The processing device 206 includes, for example, one or more chips. As an example, the processing unit 206 is configured by a central processing unit (CPU). Some or all of the functions of the processing device 206 may be realized by hardware such as DSP, ASIC, PLD, or FPGA. The processing device 206 executes various processes in parallel or sequentially.
　処理装置２０６は、記憶装置２０５から複数のプログラムを読み取る。処理装置２０６は、当該複数のプログラムを実行することによって、再生実行部２１０及び動作制御部２２０として機能する。例えば、処理装置２０６は、開発段階の再生用ソフトウェアを記憶装置２０５から読み取る。処理装置２０６は、開発段階の再生用ソフトウェアを実行することによって、再生実行部２１０として機能する。 The processing device 206 reads a plurality of programs from the storage device 205. The processing device 206 functions as the reproduction executing unit 210 and the operation control unit 220 by executing the plurality of programs. For example, the processing device 206 reads the reproduction software at the development stage from the storage device 205. The processing device 206 functions as the reproduction executing unit 210 by executing the reproduction software at the development stage.
　再生実行部２１０及び動作制御部２２０は、それぞれ、処理装置２０６の機能ブロックの一例である。動作制御部２２０は、ＤＳＰ、ＡＳＩＣ、ＰＬＤ又はＦＰＧＡ等のハードウェアによって構成されてもよい。 The reproduction execution unit 210 and the operation control unit 220 are examples of functional blocks of the processing device 206. The operation control unit 220 may be configured by hardware such as DSP, ASIC, PLD or FPGA.
　再生実行部２１０は、開発段階の再生用ソフトウェアに基づいて実現される。再生実行部２１０は、配信データに基づいて動画及び音声を再生する。例えば、再生実行部２１０は、通信装置２０４によって配信サーバ１０から受信される配信データ、具体的には、動画データ及び音声データの各々をデコードする。再生実行部２１０は、動画データのデコード結果である画像情報を表示装置２０２に提供することによって、表示装置２０２に、動画データの示す動画を表示させる。再生実行部２１０は、音声データのデコード結果である音声情報をスピーカ２０３に提供することによって、スピーカ２０３に、音声データの示す音声を出力させる。 The reproduction execution unit 210 is realized based on reproduction software at the development stage. The reproduction execution unit 210 reproduces a moving image and a sound based on the distribution data. For example, the reproduction executing unit 210 decodes distribution data received from the distribution server 10 by the communication device 204, specifically, each of moving image data and audio data. The reproduction executing unit 210 provides the display device 202 with the image information that is the decoding result of the moving image data, thereby causing the display device 202 to display the moving image indicated by the moving image data. The reproduction executing unit 210 provides the speaker 203 with the audio information that is the decoding result of the audio data, so that the speaker 203 outputs the audio indicated by the audio data.
　なお、配信データがＤＲＭ（Digital Rights Management）によって暗号化されている場合、再生実行部２１０は、まず、配信データをデクリプト（decrypt）する。続いて、再生実行部２１０は、デクリプトされた配信データをデコードすることによって、画像情報と音声情報とを生成する。 If the distribution data is encrypted by DRM (Digital Rights Management), the reproduction executing unit 210 first decrypts the distribution data. Subsequently, the reproduction executing unit 210 generates image information and audio information by decoding the decrypted distribution data.
　動作制御部２２０は、情報処理装置２０の動作を制御する。例えば、動作制御部２２０は、情報処理装置２０における動画の再生に関する状況を示す再生ログを、通信装置２０４から評価装置３０に送信する。再生ログは、状況情報の一例である。 The operation control unit 220 controls the operation of the information processing device 20. For example, the operation control unit 220 transmits, from the communication device 204 to the evaluation device 30, a reproduction log indicating a situation regarding reproduction of a moving image in the information processing device 20. The reproduction log is an example of status information.
　再生ログは、動画の再生に関する状況として、動画データに基づく動画を構成する連続する画像フレームのうち、再生されなかった画像フレーム（以下「不再生画像フレーム」と称する）の数の状況を示す。再生ログは、不再生画像フレームの数の状況として、例えば、連続する不再生画像フレームの数（以下「連続コマ落ち数」と称する）を示す。連続コマ落ち数は、「不再生画像フレームが連続する数」と称することもできる。一例を挙げると、インデックス番号「１」～「９」までの連続する９枚の画像フレームのうち、インデックス番号「３」～「８」までの画像フレームが再生されない場合、再生ログは、連続コマ落ち数として「６」を示す。 The playback log indicates the status of the number of unplayed image frames (hereinafter referred to as “non-playback image frames”) among the consecutive image frames that make up the video based on the video data, as the status related to the video playback. The reproduction log indicates the number of non-reproduction image frames, for example, the number of continuous non-reproduction image frames (hereinafter referred to as “number of consecutive frame drops”). The number of consecutive frames dropped can also be referred to as “the number of consecutive unreproduced image frames”. As an example, if the image frames with index numbers “3” to “8” are not played back among the nine consecutive image frames with index numbers “1” to “9”, the playback log is "6" is shown as the number of drops.
　また、再生ログは、動画の再生に関する状況として、さらに、動画と共に再生されるべき音声（音声データに基づいて再生される音声）と、動画と、の再生タイミングのズレ時間（以下「音声と動画の再生タイミングのズレ時間」と称する）を示す。音声と動画の再生タイミングのズレ時間は、音声の再生タイミングと、当該音声の再生タイミングに同期して再生されるべき動画が実際に再生されるタイミングと、のズレ時間を意味する。音声と動画の再生タイミングのズレ時間は、音声と動画の再生タイミングのズレの状況の一例である。 In addition, the reproduction log indicates a situation regarding the reproduction of the moving image, further, a time difference between the reproduction timing of the sound to be reproduced together with the moving image (the sound reproduced based on the sound data) and the moving image (hereinafter referred to as “sound and moving image”). (Referred to as “deviation timing deviation time”). The discrepancy time between the reproduction timings of the audio and the moving image means the discrepancy time between the reproduction timing of the audio and the actual reproduction timing of the moving image to be reproduced in synchronization with the reproduction timing of the audio. The time difference between the reproduction timings of the sound and the moving image is an example of the situation of the difference between the reproduction timings of the sound and the moving image.
　不再生画像フレームの数の状況と、連続コマ落ち数と、音声と動画の再生タイミングのズレの状況と、音声と動画の再生タイミングのズレ時間と、の各々は、動画の再生の乱れに関する状況の一例である。 The status of the number of unplayed image frames, the number of consecutive frames dropped, the status of audio/video playback timing deviation, and the audio/video playback timing deviation time are each related to video playback disturbance. Is an example.
　このように情報処理装置２０の再生ログは、連続コマ落ち数と、音声と動画の再生タイミングのズレ時間と、を示す。以下、情報処理装置２０の再生ログを「評価用の再生ログ」と称する。 As described above, the reproduction log of the information processing device 20 indicates the number of consecutive frames dropped and the time difference between the reproduction timings of the audio and the moving image. Hereinafter, the reproduction log of the information processing device 20 will be referred to as “evaluation reproduction log”.
＜Ａ４．評価装置３０＞
　評価装置３０は、評価用の再生ログ（情報処理装置２０の再生ログ）に基づいて、開発段階の再生用ソフトウェアを自動的に評価する。 <A4. Evaluation device 30>
The evaluation device 30 automatically evaluates the reproduction software at the development stage based on the reproduction log for evaluation (reproduction log of the information processing device 20).
　評価装置３０は、通信装置３０１、表示装置３０２、スピーカ３０３、記憶装置３０４及び処理装置３０５を含むコンピュータシステムによって構成される。評価装置３０の各要素は、１又は２以上のバスによって相互に接続される。また、評価装置３０の各要素は、１又は２以上の機器によって構成される。 The evaluation device 30 is composed of a computer system including a communication device 301, a display device 302, a speaker 303, a storage device 304, and a processing device 305. The elements of the evaluation device 30 are connected to each other by one or more buses. In addition, each element of the evaluation device 30 is configured by one or two or more devices.
　通信装置３０１は、ネットワークＮＷを介して他の装置、例えば、情報処理装置２０と通信する。通信装置３０１は、例えば、ネットワークデバイス、ネットワークコントローラ、ネットワークカード又は通信モジュールとも称される。 The communication device 301 communicates with another device, for example, the information processing device 20, via the network NW. The communication device 301 is also called, for example, a network device, a network controller, a network card, or a communication module.
　表示装置３０２は、処理装置３０５によって制御されることによって各種の画像を表示する。例えば、表示装置３０２は、開発段階の再生用ソフトウェアに対する評価結果を表す画像を表示する。表示装置３０２は、例えば、液晶表示パネルである。表示装置３０２は、液晶表示パネルに限定されない。例えば、表示装置３０２は、有機ＥＬ表示パネル等でもよい。 The display device 302 displays various images under the control of the processing device 305. For example, the display device 302 displays an image showing the evaluation result of the reproduction software at the development stage. The display device 302 is, for example, a liquid crystal display panel. The display device 302 is not limited to a liquid crystal display panel. For example, the display device 302 may be an organic EL display panel or the like.
　スピーカ３０３は、処理装置３０５によって制御されることによって各種の音を出力する。例えば、スピーカ３０３は、開発段階の再生用ソフトウェアに対する評価結果を音によって報知する。なお、スピーカ３０３は、省略されてもよい。 The speaker 303 outputs various sounds under the control of the processing device 305. For example, the speaker 303 informs the evaluation result of the reproduction software at the development stage by sound. The speaker 303 may be omitted.
　記憶装置３０４は、処理装置３０５が読み取り可能な記録媒体である。記憶装置３０４は、処理装置３０５によって実行される制御プログラムを含む複数のプログラム、及び、処理装置３０５が使用する各種のデータを記憶する。また、記憶装置３０４は、複数の係数Ｋを含む学習モデル３０４ａを記憶する。記憶装置３０４は、例えば、ＲＯＭ、ＥＰＲＯＭ、ＥＥＰＲＯＭ及びＲＡＭ等の記録媒体のうち少なくとも１つによって構成される。 The storage device 304 is a recording medium that can be read by the processing device 305. The storage device 304 stores a plurality of programs including a control program executed by the processing device 305, and various data used by the processing device 305. The storage device 304 also stores a learning model 304a including a plurality of coefficients K. The storage device 304 is configured by at least one of recording media such as ROM, EPROM, EEPROM, and RAM.
　処理装置３０５は、評価装置３０を制御するプロセッサである。処理装置３０５は、例えば、１又は２以上のチップによって構成される。一例を挙げると、処理装置３０５は、中央処理装置（ＣＰＵ）によって構成される。処理装置３０５の機能の一部又は全部は、ＤＳＰ、ＡＳＩＣ、ＰＬＤ又はＦＰＧＡ等のハードウェアによって実現されてもよい。処理装置３０５は、各種の処理を並列的又は逐次的に実行する。 The processing device 305 is a processor that controls the evaluation device 30. The processing device 305 includes, for example, one or more chips. As an example, the processing device 305 is configured by a central processing unit (CPU). Some or all of the functions of the processing device 305 may be realized by hardware such as DSP, ASIC, PLD, or FPGA. The processing device 305 executes various processes in parallel or sequentially.
　処理装置３０５は、例えば、記憶装置３０４から学習モデル３０４ａと制御プログラムを読み取る。処理装置３０５は、学習モデル３０４ａと制御プログラムを実行することによって、受取部３１０、評価部３２０及び出力部３３０として機能する。なお、評価部３２０は、学習モデル３０４ａを実行することによって実現される。受取部３１０、評価部３２０及び出力部３３０は、処理装置３０５の機能ブロックの一例である。受取部３１０、評価部３２０及び出力部３３０の全部又は一部は、ＤＳＰ、ＡＳＩＣ、ＰＬＤ又はＦＰＧＡ等のハードウェアによって構成されてもよい。 The processing device 305 reads the learning model 304a and the control program from the storage device 304, for example. The processing device 305 functions as the receiving unit 310, the evaluation unit 320, and the output unit 330 by executing the learning model 304a and the control program. The evaluation unit 320 is realized by executing the learning model 304a. The receiving unit 310, the evaluation unit 320, and the output unit 330 are examples of functional blocks of the processing device 305. All or some of the receiving unit 310, the evaluation unit 320, and the output unit 330 may be configured by hardware such as DSP, ASIC, PLD, or FPGA.
　受取部３１０は、情報処理装置２０から、評価用の再生ログ（情報処理装置２０での動画の再生に関する状況を示す再生ログ）を受け取る。 The receiving unit 310 receives an evaluation reproduction log (a reproduction log indicating a status regarding reproduction of a moving image on the information processing apparatus 20) from the information processing apparatus 20.
　評価部３２０によって用いられる学習モデル３０４ａは、動画の再生に関する状況と、動画の再生に関する状況に対する評価と、の関係を示す関係データを学習した学習済みモデルである。学習モデル３０４ａは、複数の係数Ｋによって規定される。複数の係数Ｋは、関係データの一例である教師データを利用した機械学習によって特定される。評価部３２０は、学習モデル３０４ａを用いて、受取部３１０によって受け取られる評価用の再生ログに基づいて、開発段階の再生用ソフトウェアが行う動画の再生に関する状況を評価する。 The learning model 304a used by the evaluation unit 320 is a learned model that has learned relational data indicating the relationship between the situation regarding the reproduction of the moving image and the evaluation regarding the situation regarding the reproduction of the moving image. The learning model 304a is defined by a plurality of coefficients K. The plurality of coefficients K are specified by machine learning using teacher data, which is an example of relational data. The evaluation unit 320 uses the learning model 304a to evaluate the situation regarding the reproduction of the moving image performed by the reproduction software in the development stage, based on the evaluation reproduction log received by the reception unit 310.
　学習モデル３０４ａは、例えば、ニューラルネットワーク、典型的には、ディープニューラルネットワークを規定する。評価部３２０は、処理装置（コンピュータの例示）３０５によって実現されるニューラルネットワーク、さらに言えば、機能ブロックである。評価部３２０は、入力Ａに応じた出力Ｂを生成する。 The learning model 304a defines, for example, a neural network, typically a deep neural network. The evaluation unit 320 is a neural network realized by the processing device (an example of a computer) 305, or more specifically, a functional block. The evaluation unit 320 generates the output B according to the input A.
　学習モデル３０４ａは、入力Ａから出力Ｂを特定する演算を処理装置３０５に実行させるプログラム（例えば、人工知能ソフトウェアを構成するプログラムモジュール）において利用される。具体的には、学習モデル３０４ａは、当該演算に適用される複数の係数Ｋを含む。当該演算には、所定の応答関数が用いられる。 The learning model 304a is used in a program (for example, a program module that constitutes artificial intelligence software) that causes the processing device 305 to execute an operation that specifies the output A from the input A. Specifically, the learning model 304a includes a plurality of coefficients K applied to the calculation. A predetermined response function is used for the calculation.
　複数の係数Ｋは、複数の教師データを含む関係データを利用した事前の機械学習（深層学習）によって最適化されている。複数の教師データの各々では、入力Ａと出力Ｂとが相互に対応づけられている。すなわち、学習モデル３０４ａは、入力Ａと出力Ｂとの間の関係を示す関係データを学習した統計的モデルである。 The multiple coefficients K are optimized by prior machine learning (deep learning) using relational data including multiple teacher data. In each of the plurality of teacher data, the input A and the output B are associated with each other. That is, the learning model 304a is a statistical model in which the relational data indicating the relation between the input A and the output B is learned.
　処理装置３０５は、複数の係数Ｋと所定の応答関数とを適用した演算を未知の入力Ａに対して実行することによって、複数の教師データから抽出される傾向（入力Ａと出力Ｂとの間の関係）に基づく出力B、すなわち、入力Ａに対して妥当な出力Ｂを生成する。 The processing device 305 executes a calculation that applies a plurality of coefficients K and a predetermined response function to an unknown input A, and thereby tends to be extracted from a plurality of teacher data (between the input A and the output B). Output B based on the relation (1), that is, an output B that is valid for the input A is generated.
　学習モデル３０４ａでは、入力Ａとして、「再生ログ」（動画の再生に関する状況を示す情報）が用いられ、出力Ｂとして「評価」が用いられる。 In the learning model 304a, a “playback log” (information indicating the status regarding playback of a moving image) is used as the input A, and an “evaluation” is used as the output B.
　なお、評価部３２０は、例えば、Tensor Processing Unit（テンソルプロセッシングユニット）及びNeural Engine（ニューラルエンジン）等のニューラルネットワーク用のプロセッサによって実現されてもよい。 The evaluation unit 320 may be implemented by a neural network processor such as a Tensor Processing Unit (tensor processing unit) and a Neural Engine (neural engine), for example.
　出力部３３０は、開発段階の再生用ソフトウェアに対する評価部３２０での評価結果に基づく情報を出力する。例えば、出力部３３０は、評価結果に基づく情報として、評価結果に基づく画像情報、及び、評価結果に基づく音声情報を出力する。 The output unit 330 outputs information based on the evaluation result by the evaluation unit 320 for the reproduction software at the development stage. For example, the output unit 330 outputs image information based on the evaluation result and audio information based on the evaluation result as the information based on the evaluation result.
＜Ａ５．学習モデル生成装置５０＞
　図２は、複数の係数Ｋによって規定される学習モデル３０４ａを生成する学習モデル生成装置５０の一例を示す図である。図２において、図１にて説明した要素と同一又は同様の要素については、同一の符号を付し、詳細な説明を省略する。 <A5. Learning model generation device 50>
FIG. 2 is a diagram showing an example of a learning model generation device 50 that generates a learning model 304a defined by a plurality of coefficients K. In FIG. 2, elements that are the same as or similar to the elements described in FIG. 1 are assigned the same reference numerals and detailed description thereof is omitted.
　学習モデル生成装置５０は、学習モデル３０４ａ用の教師データと、学習モデル３０４ａを規定する複数の係数Ｋと、を生成する。学習モデル３０４ａ用の教師データは、学習モデル生成装置５０とは異なる装置によって生成されてもよい。学習モデル生成装置５０は、ネットワークＮＷを介して、情報処理装置４１～４ｎ（ｎは１以上の整数）と通信する。
　情報処理装置４１～４ｎは、開発段階の再生用ソフトウェアではなく開発の完了した再生用ソフトウェアを実行することによって再生実行部２１０を実現する点、及び、動画及び音声を再現するための再生情報が付与されている再生ログを出力する点を除いて、情報処理装置２０と同一構成である。以下、情報処理装置４１～４ｎの各々を、情報処理装置４ｍ（ｍは１～ｎの整数）と称する。
　再生情報は、情報処理装置４ｍが再生した動画及び音声を再現するために使用される。再生情報は、情報処理装置４ｍにおいて音声及び動画の再生に用いられた配信データの識別情報を含む。再生情報は、さらに、動画を構成する画像フレームのインデックス番号ごとに、当該インデックス番号の画像フレームの画質（解像度）、当該画像フレームの再生の有無、及び、当該画像フレームが同期すべき音声と当該画像フレームとの再生タイミングのズレ時間を示す。 The learning model generation device 50 generates teacher data for the learning model 304a and a plurality of coefficients K that define the learning model 304a. The teacher data for the learning model 304a may be generated by a device different from the learning model generation device 50. The learning model generation device 50 communicates with the information processing devices 41 to 4n (n is an integer of 1 or more) via the network NW.
The information processing devices 41 to 4n realize the reproduction executing unit 210 by executing the reproduction software which has been developed, not the reproduction software at the development stage, and the reproduction information for reproducing the moving image and the sound. The configuration is the same as that of the information processing device 20 except that the added reproduction log is output. Hereinafter, each of the information processing devices 41 to 4n is referred to as an information processing device 4m (m is an integer of 1 to n).
The reproduction information is used to reproduce the moving image and sound reproduced by the information processing device 4m. The reproduction information includes identification information of the distribution data used for reproducing the sound and the moving image in the information processing device 4m. The reproduction information further includes, for each index number of the image frame forming the moving image, the image quality (resolution) of the image frame with the index number, whether or not the image frame is reproduced, and the audio to be synchronized with the image frame. The time difference between the reproduction timing and the image frame is shown.
　学習モデル生成装置５０は、入力装置５０１、表示装置５０２、スピーカ５０３、通信装置５０４、記憶装置５０５及び処理装置５０６を含むコンピュータシステムによって構成される。学習モデル生成装置５０の各要素は、１又は２以上のバスによって相互に接続される。また、学習モデル生成装置５０の各要素は、１又は２以上の機器によって構成される。 The learning model generation device 50 is configured by a computer system including an input device 501, a display device 502, a speaker 503, a communication device 504, a storage device 505, and a processing device 506. Each element of the learning model generation device 50 is mutually connected by one or two or more buses. Further, each element of the learning model generation device 50 is configured by one or two or more devices.
　入力装置５０１は、学習モデル生成装置５０によって使用される情報を学習モデル生成装置５０に入力するための機器である。入力装置５０１は、ユーザからの指示又は質問等のユーザの入力を受け取る。入力装置５０１は、例えば、キーボードである。なお、入力装置５０１は、キーボードに限らず、表示装置５０２の表示面に対する接触を検出するタッチパネル、マウス、スイッチ又はボタン等でもよい。 The input device 501 is a device for inputting information used by the learning model generation device 50 to the learning model generation device 50. The input device 501 receives user input such as instructions or questions from the user. The input device 501 is, for example, a keyboard. Note that the input device 501 is not limited to the keyboard, and may be a touch panel, a mouse, a switch, a button, or the like that detects contact with the display surface of the display device 502.
　表示装置５０２は、処理装置５０６によって制御されることによって各種の画像を表示する。例えば、表示装置５０２は、配信データに含まれる動画データに基づく動画を表示する。表示装置５０２は、例えば液晶表示パネル又は有機ＥＬ表示パネル等である。 The display device 502 displays various images under the control of the processing device 506. For example, the display device 502 displays a moving image based on the moving image data included in the distribution data. The display device 502 is, for example, a liquid crystal display panel or an organic EL display panel.
　スピーカ５０３は、処理装置５０６によって制御されることによって各種の音を出力する。例えば、スピーカ５０３は、配信データに含まれる音声データに基づく音声を出力する。 The speaker 503 outputs various sounds by being controlled by the processing device 506. For example, the speaker 503 outputs a sound based on the sound data included in the distribution data.
　通信装置５０４は、ネットワークＮＷを介して他の装置、例えば、情報処理装置４１～４ｎと通信する。通信装置５０４は、例えば、ネットワークデバイス、ネットワークコントローラ、ネットワークカード又は通信モジュールとも称される。 The communication device 504 communicates with other devices, for example, the information processing devices 41 to 4n, via the network NW. The communication device 504 is also called, for example, a network device, a network controller, a network card, or a communication module.
　記憶装置５０５は、処理装置５０６が読み取り可能な記録媒体である。記憶装置５０５は、処理装置５０６によって実行される制御プログラム、及び、処理装置５０６が使用する各種のデータを記憶する。記憶装置５０５は、例えば、不揮発性メモリ（例えば、ＲＯＭ、ＥＰＲＯＭ及びＥＥＰＲＯＭ）と、揮発性メモリ（例えば、ＲＡＭ）とによって構成される。 The storage device 505 is a recording medium that can be read by the processing device 506. The storage device 505 stores a control program executed by the processing device 506 and various data used by the processing device 506. The storage device 505 is composed of, for example, a non-volatile memory (for example, ROM, EPROM, and EEPROM) and a volatile memory (for example, RAM).
　処理装置５０６は、学習モデル生成装置５０を制御するプロセッサである。処理装置５０６は、例えば、１又は２以上のチップによって構成される。一例を挙げると、処理装置５０６は、中央処理装置（ＣＰＵ）によって構成される。処理装置５０６の機能の一部又は全部は、ＤＳＰ、ＡＳＩＣ、ＰＬＤ又はＦＰＧＡ等のハードウェアによって実現されてもよい。処理装置５０６は、各種の処理を並列的又は逐次的に実行する。 The processing device 506 is a processor that controls the learning model generation device 50. The processing device 506 includes, for example, one or more chips. As an example, the processing device 506 is configured by a central processing unit (CPU). Some or all of the functions of the processing device 506 may be realized by hardware such as DSP, ASIC, PLD, or FPGA. The processing device 506 executes various processes in parallel or sequentially.
　処理装置５０６は、記憶装置５０５から制御プログラムを読み取る。処理装置５０６は、当該制御プログラムを実行することによって、受取部５１０、再現部５２０、評価入手部５３０、教師データ生成部５４０及び学習モデル生成部５５０として機能する。 The processing device 506 reads the control program from the storage device 505. The processing device 506 functions as the receiving unit 510, the reproducing unit 520, the evaluation obtaining unit 530, the teacher data generating unit 540, and the learning model generating unit 550 by executing the control program.
　受取部５１０、再現部５２０、評価入手部５３０、教師データ生成部５４０及び学習モデル生成部５５０は、処理装置５０６の機能ブロックの一例である。受取部５１０、再現部５２０、評価入手部５３０、教師データ生成部５４０及び学習モデル生成部５５０の全部又は一部は、ＤＳＰ、ＡＳＩＣ、ＰＬＤ又はＦＰＧＡ等のハードウェアによって構成されてもよい。 The reception unit 510, the reproduction unit 520, the evaluation acquisition unit 530, the teacher data generation unit 540, and the learning model generation unit 550 are examples of functional blocks of the processing device 506. All or some of the receiving unit 510, the reproducing unit 520, the evaluation obtaining unit 530, the teacher data generating unit 540, and the learning model generating unit 550 may be configured by hardware such as DSP, ASIC, PLD, or FPGA.
　受取部５１０は、通信装置５０４を介して、情報処理装置４ｍから再生ログを受け取る。以下、情報処理装置４ｍから受け取られる再生ログを「学習用の再生ログ」と称する。学習用の再生ログは、連続コマ落ち数と、音声と動画の再生タイミングのズレ時間と、を示す。学習用の再生ログには、再生情報が付加されている。 The receiving unit 510 receives the reproduction log from the information processing device 4m via the communication device 504. Hereinafter, the reproduction log received from the information processing device 4m will be referred to as a "learning reproduction log". The reproduction log for learning shows the number of consecutive frames dropped and the time difference between the reproduction timings of the audio and the moving image. Replay information is added to the learning log for learning.
　再現部５２０は、学習用の再生ログごとに、再生情報に基づいて、情報処理装置４ｍにおける動画及び音声の再生の状況を、表示装置５０２及びスピーカ５０３を用いて再現する。 The reproduction unit 520 reproduces the reproduction status of the moving image and the audio in the information processing device 4m, using the display device 502 and the speaker 503, based on the reproduction information for each reproduction reproduction log for learning.
　動画の再生の品質を評価する評価者は、表示装置５０２及びスピーカ５０３を用いて再現される動画の再生について品質の評価を行う。評価者は、動画の再生の品質についての評価結果を入力装置５０１に入力する。 The evaluator who evaluates the reproduction quality of the moving image evaluates the quality of the reproduction of the moving image reproduced using the display device 502 and the speaker 503. The evaluator inputs the evaluation result of the reproduction quality of the moving image into the input device 501.
　評価入手部５３０は、学習用の再生ログごとに、動画の再生の品質についての評価結果を入力装置５０１から受け取る。 The evaluation acquisition unit 530 receives, from the input device 501, the evaluation result regarding the reproduction quality of the moving image for each learning reproduction log.
　教師データ生成部５４０は、学習用の再生ログごとに、教師データを生成する。各教師データは、学習用の再生ログと、当該学習用の再生ログに基づいて再現された動画の再生についての品質の評価結果（ラベル）と、の組である。 The teacher data generation unit 540 generates teacher data for each learning reproduction log. Each teacher data is a set of a learning reproduction log and a quality evaluation result (label) regarding reproduction of a moving image reproduced based on the learning reproduction log.
　学習モデル生成部５５０は、教師データ生成部５４０によって生成される教師データを利用した機械学習（深層学習）を行うことによって、学習モデル３０４ａを規定する複数の係数Ｋを生成する。 The learning model generation unit 550 generates a plurality of coefficients K defining the learning model 304a by performing machine learning (deep learning) using the teacher data generated by the teacher data generation unit 540.
＜Ａ６．学習モデル生成装置５０の動作＞
　図３は、学習モデル生成装置５０の動作の一例を説明するためのフローチャートである。以下、説明の簡略化のため、学習モデル生成装置５０と情報処理装置４ｍとの通信動作において通信装置２０４及び５０４の言及を省略する。 <A6. Operation of Learning Model Generation Device 50>
FIG. 3 is a flowchart for explaining an example of the operation of the learning model generation device 50. Hereinafter, for simplification of description, reference to the communication devices 204 and 504 will be omitted in the communication operation between the learning model generation device 50 and the information processing device 4m.
　受取部５１０は、情報処理装置４ｍから学習用の再生ログを受け取る（ステップＳ１００）。 The receiving unit 510 receives the learning reproduction log from the information processing device 4m (step S100).
　ステップＳ１００においては、例えば、受取部５１０は、情報処理装置４ｍが自発的に送信する学習用の再生ログを受け取る。受取部５１０は、情報処理装置４ｍに学習用の再生ログの要求を送信し、情報処理装置４ｍが学習用の再生ログの要求に応じて送信する学習用の再生ログを受け取ってもよい。 In step S100, for example, the receiving unit 510 receives a learning reproduction log that the information processing device 4m voluntarily transmits. The receiving unit 510 may transmit a request for a learning reproduction log to the information processing apparatus 4m, and may receive a learning reproduction log transmitted by the information processing apparatus 4m in response to the learning reproduction log request.
　続いて、再現部５２０は、学習用の再生ログごとに、再生情報に基づいて、情報処理装置４ｍにおける動画及び音声の再生の状況を再現する（ステップＳ１０２）。 Subsequently, the reproduction unit 520 reproduces the reproduction status of the moving image and the audio in the information processing device 4m based on the reproduction information for each learning reproduction log (step S102).
　ステップＳ１０２においては、再現部５２０は、まず、再生情報が示す配信データの識別情報によって特定される配信データを配信サーバ１０から入手する。具体的には、再現部５２０は、再生情報において画像フレームのインデックス番号ごとに示される画質のうち最高の画質を有する動画を示す配信データを、配信サーバ１０から入手する。なお、再現部５２０は、当該配信データを、配信サーバ１０とは異なるサーバから入手してもよい。
　続いて、再現部５２０は、配信データと再生情報とに基づいて、再生情報が示す動画及び音声の再生の状況を、表示装置５０２及びスピーカ５０３を用いて再現する。このため、各画像フレームの画質、各画像フレームの再生の有無、及び、各画像フレームの音声との再生タイミングのズレ時間が再現される。なお、画像フレームのインデックス番号ごとに示される画質のうち最高の画質を有する動画を示す配信データを用いるため、画像フレームのインデックス番号ごとに示されるいずれの画質への変換も可能である。 In step S102, the reproduction unit 520 first obtains from the distribution server 10 the distribution data specified by the identification information of the distribution data indicated by the reproduction information. Specifically, the reproduction unit 520 acquires, from the distribution server 10, distribution data indicating a moving image having the highest image quality among the image quality indicated for each index number of the image frame in the reproduction information. The reproduction unit 520 may obtain the distribution data from a server different from the distribution server 10.
Subsequently, the reproduction unit 520 reproduces the reproduction state of the moving image and the audio indicated by the reproduction information using the display device 502 and the speaker 503 based on the distribution data and the reproduction information. Therefore, the image quality of each image frame, the presence or absence of reproduction of each image frame, and the time difference between the reproduction timing of the sound of each image frame and the sound are reproduced. Since the distribution data indicating the moving image having the highest image quality among the image quality indicated for each image frame index number is used, the conversion to any image quality indicated for each image frame index number is possible.
　評価者は、表示装置５０２及びスピーカ５０３を用いて再現される動画の再生の品質を評価し、評価結果を入力装置５０１に入力する。評価入手部５３０は、入力装置５０１に入力された評価結果を受け取る（ステップＳ１０４）。 The evaluator evaluates the reproduction quality of the moving image reproduced using the display device 502 and the speaker 503, and inputs the evaluation result to the input device 501. The evaluation acquisition unit 530 receives the evaluation result input to the input device 501 (step S104).
　続いて、教師データ生成部５４０は、学習用の再生ログから再生情報を削除する。次に、教師データ生成部５４０は、再生情報が削除された学習用の再生ログごとに、当該学習用の再生ログと、当該学習用の再生ログに基づき再現された動画の再生の品質の評価結果と、の組を、教師データとして生成する（ステップＳ１０６）。このため、教師データは、動画の再生に関する状況（再生ログが示す状況）と、動画の再生に関する状況に対する評価（評価結果）と、の関係を示す。 Subsequently, the teacher data generation unit 540 deletes the reproduction information from the reproduction reproduction log for learning. Next, the teacher data generation unit 540 evaluates the learning reproduction log for each learning reproduction log in which the reproduction information is deleted, and the reproduction quality of the moving image reproduced based on the learning reproduction log. A pair of the result and is generated as teacher data (step S106). Therefore, the teacher data indicates the relationship between the situation regarding the reproduction of the moving image (the situation indicated by the reproduction log) and the evaluation regarding the situation regarding the reproduction of the moving image (evaluation result).
　続いて、学習モデル生成部５５０は、教師データ生成部５４０にて生成される教師データを利用した機械学習（深層学習）を行うことによって（ステップＳ１０８）、学習モデル３０４ａを規定する複数の係数Ｋを特定する（ステップＳ１１０）。 Subsequently, the learning model generation unit 550 performs machine learning (deep learning) using the teacher data generated by the teacher data generation unit 540 (step S108), and thereby a plurality of coefficients K defining the learning model 304a. Is specified (step S110).
　学習モデル生成部５５０にて特定された複数の係数Ｋは、例えば、評価者によって、図１に示す記憶装置３０４に記憶される。記憶装置３０４に記憶された複数の係数Ｋに基づいて、評価装置３０は評価部３２０を実現する。
　以上の説明から理解される通り、学習モデル３０４ａは、動画の再生に関する状況と、動画の再生に関する状況に対する評価と、の関係を学習する。 The plurality of coefficients K specified by the learning model generation unit 550 are stored in the storage device 304 shown in FIG. 1 by the evaluator, for example. The evaluation device 30 implements the evaluation unit 320 based on the plurality of coefficients K stored in the storage device 304.
As can be understood from the above description, the learning model 304a learns the relationship between the situation regarding the reproduction of the moving image and the evaluation regarding the situation regarding the reproduction of the moving image.
＜Ａ７．評価装置３０の動作＞
　図４は、評価装置３０の動作の一例について説明するためのフローチャートである。以下、説明の簡略化のため、評価装置３０と情報処理装置２０との通信において通信装置２０４及び３０１についての言及を省略する。 <A7. Operation of Evaluation Device 30>
FIG. 4 is a flowchart for explaining an example of the operation of the evaluation device 30. Hereinafter, in order to simplify the description, the reference to the communication devices 204 and 301 in the communication between the evaluation device 30 and the information processing device 20 will be omitted.
　受取部３１０は、情報処理装置２０から評価用の再生ログを受け取る（ステップＳ２００）。 The receiving unit 310 receives the reproduction log for evaluation from the information processing device 20 (step S200).
　ステップＳ２００においては、例えば、受取部３１０は、情報処理装置２０が自発的に送信する評価用の再生ログを受け取る。受取部３１０は、情報処理装置２０に評価用の再生ログの要求を送信し、情報処理装置２０が評価用の再生ログの要求に応じて送信する評価用の再生ログを受け取ってもよい。 In step S200, for example, the receiving unit 310 receives the reproduction log for evaluation that the information processing apparatus 20 voluntarily transmits. The receiving unit 310 may send a request for a reproduction log for evaluation to the information processing apparatus 20, and receive the reproduction log for evaluation transmitted by the information processing apparatus 20 in response to the request for the reproduction log for evaluation.
　続いて、評価部３２０は、学習モデル３０４ａを用いて、受取部３１０が受け取る評価用の再生ログに基づいて、開発段階の再生用ソフトウェアが行う動画の再生に関する状況を評価する（ステップＳ２０２）。 Subsequently, the evaluation unit 320 uses the learning model 304a to evaluate the situation regarding the reproduction of the moving image performed by the reproduction software in the development stage based on the reproduction log received by the reception unit 310 (step S202).
　ステップＳ２０２においては、評価部３２０は、評価用の再生ログを入力として、開発段階の再生用ソフトウェアが行う動画の再生に関する状況の評価を出力する。なお、受取部３１０が、評価用の再生ログを評価部３２０に入力してもよい。 In step S202, the evaluation unit 320 receives the evaluation reproduction log as an input, and outputs the evaluation of the situation regarding the reproduction of the moving image performed by the reproduction software in the development stage. The receiving unit 310 may input the reproduction log for evaluation to the evaluation unit 320.
　続いて、出力部３３０は、開発段階の再生用ソフトウェアに対して評価部３２０が行う評価の結果に基づく情報を出力する（ステップＳ２０４）。 Subsequently, the output unit 330 outputs information based on the result of the evaluation performed by the evaluation unit 320 on the reproduction software at the development stage (step S204).
　ステップＳ２０４においては、出力部３３０は、評価部３２０が行う評価の結果を示す評価画像情報を生成し、評価画像情報を表示装置３０２に出力する。表示装置３０２は、評価画像情報を受け取ると、評価画像情報が示す画像（評価部３２０が行う評価の結果を示す画像）を表示する。 In step S204, the output unit 330 generates evaluation image information indicating the result of the evaluation performed by the evaluation unit 320, and outputs the evaluation image information to the display device 302. Upon receiving the evaluation image information, the display device 302 displays the image indicated by the evaluation image information (the image indicating the result of the evaluation performed by the evaluation unit 320).
　一例を挙げると、評価部３２０が行う評価の結果が、再生の品質が悪いことを示す場合、出力部３３０は、開発段階の再生用ソフトウェアが行う再生の品質が悪いことを第１画像によって示す第１評価画像情報を、表示装置３０２に出力する。第１画像は、例えば「再生品質が悪いです。」と言う文字を表す。なお、第１画像は、「再生品質が悪いです。」という文字を表す画像に限らず、例えば「×」という記号を表す画像でもよく、適宜変更可能である。表示装置３０２は、第１評価画像情報を受け取ると、第１画像を表示する。 For example, when the evaluation result performed by the evaluation unit 320 indicates that the reproduction quality is poor, the output unit 330 indicates by the first image that the reproduction software performed in the development stage has poor reproduction quality. The first evaluation image information is output to the display device 302. The first image represents, for example, a character "reproduction quality is poor." Note that the first image is not limited to the image representing the character "reproduction quality is poor.", but may be an image representing the symbol "x", for example, and can be appropriately changed. Upon receiving the first evaluation image information, the display device 302 displays the first image.
　一方、評価部３２０が行う評価結果が、再生の品質が良いことを示す場合、出力部３３０は、開発段階の再生用ソフトウェアが行う再生の品質が良いことを第２画像によって示す第２評価画像情報を、表示装置３０２に出力する。第２画像は、例えば「再生品質が良いです。」と言う文字を表す。なお、第２画像は、「再生品質が良いです。」という文字を表す画像に限らず、例えば「○」という記号を表す画像でもよく、適宜変更可能である。表示装置３０２は、第２評価画像情報を受け取ると、第２画像を表示する。 On the other hand, when the evaluation result performed by the evaluation unit 320 indicates that the quality of reproduction is good, the output unit 330 uses the second evaluation image that indicates that the quality of reproduction performed by the reproduction software in the development stage is good by the second image. The information is output to the display device 302. The second image represents, for example, a character "The reproduction quality is good." It should be noted that the second image is not limited to the image showing the character “The reproduction quality is good.”, but may be an image showing the symbol “◯”, for example, and can be changed appropriately. Upon receiving the second evaluation image information, the display device 302 displays the second image.
　また、ステップＳ２０４においては、出力部３３０は、評価部３２０が行う評価の結果に基づく評価音声情報を生成し、評価音声情報をスピーカ３０３に出力する。スピーカ３０３は、評価音声情報を受け取ると、評価音声情報に応じた音を出力する。 Further, in step S204, the output unit 330 generates evaluation voice information based on the result of the evaluation performed by the evaluation unit 320, and outputs the evaluation voice information to the speaker 303. Upon receiving the evaluation voice information, the speaker 303 outputs a sound according to the evaluation voice information.
　一例を挙げると、評価部３２０が行う評価の結果が、再生の品質が悪いことを示す場合、出力部３３０は、開発段階の再生用ソフトウェアが行う再生の品質が悪いことを第１音声によって表現する第１評価音声情報を、スピーカ３０３に出力する。第１音声は、例えば、「再生品質が悪いです。」という音声である。なお、第１音声は、「再生品質が悪いです。」という音声に限らず、例えば、「ＮＧ」という音声でもよく、適宜変更可能である。スピーカ３０３は、第１評価音声情報を受け取ると、第１評価音声情報に応じた第１音声を出力する。 For example, when the evaluation result performed by the evaluation unit 320 indicates that the reproduction quality is poor, the output unit 330 expresses by the first voice that the reproduction quality performed by the reproduction software in the development stage is poor. The first evaluation voice information to be output is output to the speaker 303. The first voice is, for example, a voice "reproduction quality is poor." The first voice is not limited to the voice "reproduction quality is poor." Upon receiving the first evaluation voice information, the speaker 303 outputs the first voice according to the first evaluation voice information.
　一方、評価部３２０が行う評価の結果が、再生の品質が良いことを示す場合、出力部３３０は、開発段階の再生用ソフトウェアが行う再生の品質が良いことを第２音声によって表現する第２評価音声情報を、スピーカ３０３に出力する。第２音声は、例えば、「再生品質が良いです。」という音声である。なお、第２音声は、「再生品質が良いです。」という音声に限らず、例えば、「ＯＫ」という音声でもよく適宜変更可能である。スピーカ３０３は、第２評価音声情報を受け取ると、第２評価音声情報に応じた第２音声を出力する。 On the other hand, when the result of the evaluation performed by the evaluation unit 320 indicates that the reproduction quality is good, the output unit 330 expresses by the second voice that the reproduction quality performed by the reproduction software in the development stage is good. The evaluation voice information is output to the speaker 303. The second sound is, for example, a sound "the reproduction quality is good." The second voice is not limited to the voice "The reproduction quality is good", but may be the voice "OK", for example, and can be changed as appropriate. Upon receiving the second evaluation audio information, the speaker 303 outputs the second audio corresponding to the second evaluation audio information.
　なお、ステップＳ２０４において、出力部３３０は、評価部３２０が行う評価の結果に基づく情報として、評価結果を示す電子ファイルを他の装置に出力してもよい。評価結果を示す電子ファイルとしては、例えば、評価結果を示すテキストデータの電子ファイル、及び、評価結果を示す表計算用の電子ファイルが用いられる。 Note that in step S204, the output unit 330 may output an electronic file indicating the evaluation result to another device as information based on the result of the evaluation performed by the evaluation unit 320. As the electronic file showing the evaluation result, for example, an electronic file of text data showing the evaluation result and an electronic file for spreadsheet showing the evaluation result are used.
＜Ａ８．第１実施形態についてのまとめ＞
　本実施形態によれば、評価部３２０が、動画の再生に関する状況と当該状況に対する評価との関係を示す関係データを学習した学習モデル３０４ａを用いて、再生用ソフトウェアが行う動画の再生に関する再生状況を評価する。このため、人が、開発段階の再生用ソフトウェアが行う動画の再生の状況を評価する場合に比べて、再生用ソフトウェアの開発段階におけるテストの手間を低減できる。 <A8. Summary of First Embodiment>
According to the present embodiment, the evaluation unit 320 uses the learning model 304a that learned the relational data indicating the relationship between the situation regarding the reproduction of the moving image and the evaluation for the situation, and the reproduction situation regarding the reproduction of the moving image performed by the reproduction software. Evaluate. For this reason, compared to a case where a person evaluates the reproduction state of a moving image performed by the reproduction software in the development stage, the labor of the test in the development stage of the reproduction software can be reduced.
　受取部３１０が、再生用ソフトウェアが行う動画の再生に関する状況を示す評価用の再生ログを受け取り、評価部３２０が、受取部３１０が受け取る評価用の再生ログが示す再生状況に応じた評価結果を生成する。このため、評価装置３０が評価用の再生ログを生成する必要がないという利点がある。 The receiving unit 310 receives a playback log for evaluation indicating the status regarding playback of a moving image performed by the playback software, and the evaluation unit 320 displays the evaluation result according to the playback status indicated by the playback log for evaluation received by the receiving unit 310. To generate. Therefore, there is an advantage that the evaluation device 30 does not need to generate the reproduction log for evaluation.
　動画の再生に関する状況は、不再生画像フレームの数の状況、及び、音声と動画の再生タイミングのズレの状況、の両方を含む。不再生画像フレームの数が多い程、動画の再生の品質は悪くなりやすい。音声と動画の再生タイミングのズレ時間が長い程、動画の再生の品質は悪くなりやすい。このため、不再生画像フレームの数の状況、及び、音声と動画の再生タイミングのズレの状況、のいずれも含まない動画の再生に関する状況と、その評価と、の関係を学習した学習モデルを用いる評価に比べて、評価部３２０が行う評価の信頼性を高くできる。 The status regarding the playback of the moving picture includes both the status of the number of unplayed image frames and the status of the timing difference between the playback timing of the audio and the moving picture. The higher the number of non-reproduced image frames, the worse the reproduction quality of the moving image. The longer the time difference between the playback timings of audio and video, the worse the quality of video playback. For this reason, a learning model is used that learns the relationship between the situation of the number of unreproduced image frames and the situation of the reproduction of the video that does not include the situation of the reproduction timing of the audio and the video, and the evaluation thereof. The reliability of the evaluation performed by the evaluation unit 320 can be increased as compared with the evaluation.
　なお、動画の再生に関する状況が、不再生画像フレームの数の状況、及び、音声と動画の再生タイミングのズレの状況、のいずれか一方を含んでいれば、いずれも含まない動画の再生に関する状況と、その評価と、の関係を学習した学習モデルを用いる評価に比べて、評価部３２０が行う評価の信頼性を高くできる。 If the status related to video playback includes one of the status of the number of non-playback image frames and the status of timing difference between audio and video playback, status related to video playback that does not include either The reliability of the evaluation performed by the evaluation unit 320 can be made higher than the evaluation using the learning model in which the relationship between the evaluation and the evaluation is learned.
　不再生画像フレームの数の状況は、不再生画像フレームが連続する数を示す。不再生画像フレームが連続する数が多いほど、動画の再生の品質は悪くなりやすい。このため、不再生画像フレームが連続する数を含まない動画の再生に関する状況と、その評価と、の関係を学習した学習モデルを用いる評価に比べて、評価部３２０が行う評価の信頼性を高くできる。 The status of the number of non-playback image frames indicates the number of consecutive non-playback image frames. The greater the number of consecutive non-reproduced image frames, the worse the reproduction quality of the moving image. Therefore, the reliability of the evaluation performed by the evaluation unit 320 is higher than that in the evaluation using the learning model in which the relationship between the situation regarding the reproduction of the moving image that does not include the number of consecutive non-reproduced image frames and the evaluation thereof is learned. it can.
＜Ｂ．変形例＞
　以上に例示した態様に関する具体的な変形の態様を以下に例示する。以下の例示から任意に選択された２個以上の態様を、相互に矛盾しない範囲において適宜に併合してもよい。 <B. Modification>
A specific mode of modification of the above-exemplified mode will be described below. Two or more aspects arbitrarily selected from the following exemplifications may be appropriately merged as long as they do not conflict with each other.
＜Ｂ１．第１変形例＞
　第１実施形態において、再生ログが示す動画の再生に関する状況は、さらに、動画を再生している装置の負荷の状況を含んでもよい。以下、動画を再生している装置（以下「再生装置」と称する）の負荷の状況を「装置の負荷状況」とも称する。 <B1. First Modification>
In the first embodiment, the situation regarding the reproduction of the moving image indicated by the reproduction log may further include the state of the load of the device that is reproducing the moving image. Hereinafter, the load status of a device playing a moving image (hereinafter referred to as “playback device”) is also referred to as “device load status”.
　装置の負荷状況は、再生装置が配信データのデコードに要するデコード時間（以下、単に「デコード時間」と称する）を示す。一般的に、音声データのデコード時間よりも、動画データのデコード時間の方が長いため、装置の負荷状況に含まれるデコード時間として、動画データのデコードに要する時間が用いられる。 The load status of the device indicates the decoding time required for the playback device to decode the distribution data (hereinafter, simply referred to as “decoding time”). Generally, since the decoding time of moving image data is longer than the decoding time of audio data, the time required for decoding moving image data is used as the decoding time included in the load status of the device.
　装置の負荷状況は、さらに、再生装置が配信データの暗号化（例えば、ＤＲＭを用いる暗号化）の解除であるデクリプトに要する暗号解除時間（以下、「デクリプト時間」と称する）を示す。なお、配信データの暗号化は、ＤＲＭを用いる暗号化に限らない。一般的に、音声データのデクリプト時間よりも、動画データのデクリプト時間の方が長いため、装置の負荷状況に含まれるデクリプト時間として、動画データのデクリプト時間が用いられる。 The device load status further indicates a decryption time (hereinafter, referred to as “decrypt time”) required for decryption, which is a reproduction device decrypting distribution data encryption (for example, encryption using DRM). The encryption of distribution data is not limited to the encryption using DRM. Generally, since the decryption time of moving image data is longer than the decryption time of audio data, the decryption time of moving image data is used as the decryption time included in the load status of the device.
　装置の負荷状況は、デコード時間及びデクリプト時間の両方ではなく、一方のみを含んでもよい。 -The load status of the device may include only one of the decoding time and the decryption time, not both.
　また、再生ログが示す動画の再生に関する状況は、さらに、動画データのビットレートを示してもよい。 Also, the status regarding the playback of the video indicated by the playback log may further indicate the bit rate of the video data.
　第１変形例においては、学習用の再生ログと評価用の再生ログの各々は、連続コマ落ち数と、音声と動画の再生タイミングのズレ時間と、動画データのビットレートと、デコード時間と、デクリプト時間と、を示す。 In the first modification, each of the learning reproduction log and the evaluation reproduction log includes the number of consecutive frames dropped, the time difference between the reproduction timings of audio and video, the bit rate of video data, and the decoding time. And the decryption time.
　デコード時間が長いほど、連続コマ落ち数が多くなりやすい。デコード時間が長いほど、又、音声と動画の再生タイミングのズレ時間も長くなりやすい。よって、デコード時間が長いほど、動画の再生の品質悪くなりやすい。 -The longer the decoding time, the more likely the number of consecutive frames dropped. The longer the decoding time, the longer the time lag between the audio and video playback timings tends to be. Therefore, the longer the decoding time, the worse the reproduction quality of the moving image.
　デクリプト時間が長いほど、連続コマ落ち数が多くなりやすい。デクリプト時間が長いほど、音声と動画の再生タイミングのズレ時間も長くなりやすい。よって、デクリプト時間が長いほど、動画の再生品質は悪くなりやすい。 -The longer the decryption time, the more likely the number of consecutive frames dropped. The longer the decryption time, the longer the time difference between the audio and video playback timing. Therefore, the longer the decryption time, the worse the reproduction quality of the moving image.
　一方、再生用ソフトウェアの性能が高くても、動画データのビットレートが高いと、連続コマ落ち数が多くなりやすく、音声と動画の再生タイミングのズレ時間が長くなりやすく、かつ、デコード時間及びデクリプト時間が長くなりやすい。 On the other hand, even if the performance of the playback software is high, if the bit rate of the video data is high, the number of consecutive dropped frames tends to increase, the time difference between the audio and video playback timing tends to increase, and the decoding time and decryption Time tends to be long.
　このため、連続コマ落ち数と、音声と動画の再生タイミングのズレ時間と、動画データのビットレートと、デコード時間と、デクリプト時間は、動画の再生の品質に影響すると考えられる。 Therefore, it is considered that the number of consecutive dropped frames, the time difference between audio and video playback timing, the video data bit rate, the decoding time, and the decryption time affect the video playback quality.
　第１変形例においては、学習モデル３０４ａは、連続コマ落ち数、音声と動画の再生タイミングのズレ時間、動画データのビットレート、デコード時間及びデクリプト時間とを示す再生ログと、評価と、の関係を示す関係データを学習する。一方、第１実施形態の学習モデル３０４ａは、連続コマ落ち数及び音声と動画の再生タイミングのズレ時間の両方のみ又は一方のみを示す再生ログと、評価と、の関係を示す関係データを学習する。このため、第１変形例における評価部３２０が行う評価の結果は、第１実施形態の評価部３２０が行う評価の結果よりも信頼性が高くなる。 In the first modified example, the learning model 304a has a relationship between the evaluation and the reproduction log indicating the number of consecutive frames dropped, the time difference between the audio and video reproduction timing, the video data bit rate, the decoding time, and the decryption time. To learn the relational data. On the other hand, the learning model 304a of the first embodiment learns relational data indicating the relation between the evaluation and the reproduction log indicating only the number of continuous frame dropouts and/or the time difference between the reproduction timings of audio and moving images. .. Therefore, the result of the evaluation performed by the evaluation unit 320 in the first modification has higher reliability than the result of the evaluation performed by the evaluation unit 320 in the first embodiment.
　なお、学習用の再生ログと評価用の再生ログは、連続コマ落ち数、音声と動画の再生タイミングのズレ時間、動画データのビットレート、デコード時間及びデクリプト時間の全部ではなく一部のみを示してもよい。 Note that the playback log for learning and the playback log for evaluation show only the number of consecutive frames dropped, the time difference between the playback timing of audio and video, the bit rate of video data, the decoding time, and the decryption time, but not all. May be.
　例えば、学習用の再生ログと評価用の再生ログは、連続コマ落ち数と、音声と動画の再生タイミングのズレ時間と、の両方又は一方に加えて、デコード時間と、動画データのビットレートと、デクリプト時間とのうち、一つ又は二つのみを示してもよい。 For example, the playback log for learning and the playback log for evaluation include a decoding time and a bit rate of video data in addition to one or both of the number of consecutive dropped frames and the time difference between the audio and video playback timings. , Decryption time, only one or two may be shown.
　また、学習用の再生ログと評価用の再生ログは、連続コマ落ち数と、音声と動画の再生タイミングのズレ時間と、の一方のみの状況に加えて、デコード時間と、動画データのビットレートと、デクリプト時間との全部を示してもよい。 In addition, the playback log for learning and the playback log for evaluation include the decoding time and the bit rate of the video data in addition to the number of consecutive frames dropped and the time difference between the audio and video playback timing. , And the decryption time may all be shown.
　これらの場合も、不再生画像フレームの数の状況、及び、音声と動画の再生タイミングのズレの状況、のいずれも含まない動画の再生に関する状況と、その評価と、の関係を学習した学習モデルを用いる評価に比べて、評価部３２０が行う評価の信頼性を上げることができる。 Also in these cases, the learning model that learned the relationship between the situation of the number of unreproduced image frames and the situation of the reproduction of the video that does not include the situation of the timing difference between the reproduction timing of the audio and the video and the evaluation thereof. The reliability of the evaluation performed by the evaluation unit 320 can be increased as compared with the evaluation using.
＜Ｂ２．第２変形例＞
　第１実施形態及び第１変形例において、図１に例示される評価装置３０が、図２に例示される学習モデル生成装置５０の機能を含んでもよい。 <B2. Second Modification>
In the first embodiment and the first modified example, the evaluation device 30 illustrated in FIG. 1 may include the function of the learning model generation device 50 illustrated in FIG. 2.
　図５は、学習モデル生成装置５０の機能を含む評価装置３０の一例を示す図である。図５において、図１又は図２にて説明した要素と同一又は同様の要素については、同一の符号を付し、詳細な説明を省略する。 FIG. 5 is a diagram showing an example of the evaluation device 30 including the function of the learning model generation device 50. 5, elements that are the same as or similar to the elements described in FIG. 1 or FIG. 2 are given the same reference numerals, and detailed description thereof will be omitted.
　図５に例示される評価装置３０においては、受取部３１０が受取部５１０を兼用する。通信装置３０１が通信装置５０４を兼用する。表示装置３０２が表示装置５０２を兼用する。スピーカ３０３がスピーカ５０３を兼用する。記憶装置３０４が記憶装置５０５を兼用する。処理装置３０５が処理装置５０６を兼用する。 In the evaluation device 30 illustrated in FIG. 5, the receiving unit 310 also serves as the receiving unit 510. The communication device 301 also serves as the communication device 504. The display device 302 also serves as the display device 502. The speaker 303 also serves as the speaker 503. The storage device 304 also serves as the storage device 505. The processing device 305 also serves as the processing device 506.
　処理装置３０５は、記憶装置３０４に記憶されている制御プログラムおよび学習モデル３０４ａを読み取る。処理装置３０５は、当該制御プログラムおよび学習モデル３０４ａを実行することによって、受取部３１０、評価部３２０、出力部３３０、再現部５２０、評価入手部５３０、教師データ生成部５４０及び学習モデル生成部５５０として機能する。 The processing device 305 reads the control program and the learning model 304a stored in the storage device 304. The processing device 305 executes the control program and the learning model 304a to obtain the receiving unit 310, the evaluation unit 320, the output unit 330, the reproduction unit 520, the evaluation acquisition unit 530, the teacher data generation unit 540, and the learning model generation unit 550. Function as.
　第２変形例によれば、学習モデル生成部５５０が特定した複数の係数Ｋを、学習モデル生成部５５０が、記憶装置３０４に記憶できる。このため、学習モデル生成部５５０が特定した複数の係数Ｋを、人（例えば、評価者）が、記憶装置３０４に記憶させる手間を解消できる。 According to the second modification, the learning model generation unit 550 can store the plurality of coefficients K specified by the learning model generation unit 550 in the storage device 304. Therefore, a person (for example, an evaluator) can save the trouble of storing the plurality of coefficients K specified by the learning model generation unit 550 in the storage device 304.
＜Ｂ３．第３変形例＞
　第１実施形態及び第１変形例～第２変形例において、評価装置３０は、さらに、振動装置を含んでもよい。出力部３３０は、評価部３２０が行う評価結果に基づく振動情報を用いて振動装置を振動させてもよい。例えば、評価部３２０が行う評価の結果が、再生の品質が悪いことを示す場合、出力部３３０は、振動装置を振動させる振動情報を、振動装置に出力する。一方、評価部３２０が行う評価の結果が、再生の品質が良いことを示す場合、出力部３３０は、振動装置を振動させる振動情報を、振動装置に出力しない。 <B3. Third Modification>
In the first embodiment and the first and second modified examples, the evaluation device 30 may further include a vibration device. The output unit 330 may vibrate the vibrating device using the vibration information based on the evaluation result performed by the evaluation unit 320. For example, when the result of the evaluation performed by the evaluation unit 320 indicates that the reproduction quality is poor, the output unit 330 outputs the vibration information that causes the vibration device to vibrate, to the vibration device. On the other hand, when the result of the evaluation performed by the evaluation unit 320 indicates that the quality of reproduction is good, the output unit 330 does not output the vibration information for vibrating the vibration device to the vibration device.
＜Ｂ４．第４変形例＞
　第１実施形態及び第１変形例～第３変形例において、情報処理装置２０における評価用の再生ログを蓄積する装置（以下「第１蓄積装置」と称する）が存在する場合、受取部３１０は、第１蓄積装置から情報処理装置２０の評価用の再生ログを受け取ってもよい。 <B4. Fourth Modification>
In the first embodiment and the first to third modifications, if there is a device (hereinafter, referred to as “first storage device”) that stores the reproduction log for evaluation in the information processing device 20, the receiving unit 310 The reproduction log for evaluation of the information processing device 20 may be received from the first storage device.
　例えば、受取部３１０は、第１蓄積装置が自発的に送信する評価用の再生ログを受け取る。なお、受取部３１０は、第１蓄積装置に評価用の再生ログの要求を送信し、第１蓄積装置が評価用の再生ログの要求に応じて送信する評価用の再生ログを受け取ってもよい。 For example, the receiving unit 310 receives the reproduction log for evaluation that the first storage device voluntarily transmits. Note that the receiving unit 310 may send a request for a playback log for evaluation to the first storage device, and may receive a playback log for evaluation sent by the first storage device in response to the request for a playback log for evaluation. ..
＜Ｂ５．第５変形例＞
　第１実施形態及び第１変形例～第４変形例において、情報処理装置４ｍにおける学習用の再生ログを蓄積する装置（以下「第２蓄積装置」と称する）が存在する場合、受取部５１０は、第２蓄積装置から情報処理装置４ｍの学習用の再生ログを受け取ってもよい。なお、第２変形例においては、受取部５１０を兼用する受取部３１０が、第２蓄積装置から情報処理装置４ｍの学習用の再生ログを受け取ってもよい。 <B5. Fifth Modification>
In the first embodiment and the first to fourth modified examples, when there is a device (hereinafter, referred to as “second storage device”) that stores the reproduction log for learning in the information processing device 4m, the receiving unit 510 The reproduction log for learning of the information processing device 4m may be received from the second storage device. In the second modification, the receiving unit 310 that also serves as the receiving unit 510 may receive the learning reproduction log of the information processing device 4m from the second storage device.
　例えば、受取部５１０は、第２蓄積装置が自発的に送信する学習用の再生ログを受け取る。なお、受取部３１０は、第２蓄積装置に学習用の再生ログの要求を送信し、第２蓄積装置が学習用の再生ログの要求に応じて送信する学習用の再生ログを受け取ってもよい。 For example, the receiving unit 510 receives the learning reproduction log voluntarily transmitted by the second storage device. Note that the receiving unit 310 may transmit a request for a learning reproduction log to the second storage device, and may receive a learning reproduction log that the second storage device transmits in response to the request for the learning reproduction log. ..
＜Ｂ６．第６変形例＞
　第１実施形態及び第１変形例～第５変形例において、評価装置３０に、開発段階の再生用ソフトウェアがインストールされてもよい。処理装置３０５が、評価装置３０から開発段階の再生用ソフトウェアを読み取ってもよい。処理装置３０５が、評価装置３０から読み取った開発段階の再生用ソフトウェアを実行することによって、再生実行部２１０として、さらに機能してもよい。この場合、評価装置３０が、評価用の再生ログを生成できるので、情報処理装置２０を不要にできる。 <B6. Sixth Modification>
In the first embodiment and the first modification to the fifth modification, the evaluation device 30 may be installed with reproduction software at the development stage. The processing device 305 may read the reproduction software at the development stage from the evaluation device 30. The processing device 305 may further function as the reproduction executing unit 210 by executing the reproduction software at the development stage read from the evaluation device 30. In this case, since the evaluation device 30 can generate the reproduction log for evaluation, the information processing device 20 can be omitted.
＜Ｂ７．第７変形例＞
　第１実施形態及び第１変形例～第６変形例において、例えば、ＳＶＭ（Support Vector Machine）又はＨＭＭ（Hidden Markov Model）によって学習モデル３０４ａが表されてもよい。 <B7. Seventh modification>
In the first embodiment and the first to sixth modifications, the learning model 304a may be represented by, for example, an SVM (Support Vector Machine) or an HMM (Hidden Markov Model).
＜Ｃ．その他＞
　（１）第１実施形態及び第１変形例～第７変形例の各々においては、記憶装置１０２、２０５、３０４及び５０５は、フレキシブルディスク、光磁気ディスク（例えば、コンパクトディスク、デジタル多用途ディスク、Ｂｌｕ－ｒａｙ（登録商標）ディスク）、スマートカード、フラッシュメモリデバイス（例えば、カード、スティック、キードライブ）、ＣＤ－ＲＯＭ（Compact　Disc－ＲＯＭ）、レジスタ、リムーバブルディスク、ハードディスク、フロッピー（登録商標）ディスク、磁気ストリップ、データベース、サーバその他の適切な記憶媒体を含んでもよい。また、プログラムは、電気通信回線を介してネットワークから送信されてもよい。 <C. Other>
(1) In each of the first embodiment and the first to seventh modifications, the storage devices 102, 205, 304, and 505 are flexible disks, magneto-optical disks (for example, compact disks, digital versatile disks, Blu-ray (registered trademark) disk, smart card, flash memory device (for example, card, stick, key drive), CD-ROM (Compact Disc-ROM), register, removable disk, hard disk, floppy (registered trademark) disk , Magnetic strips, databases, servers and other suitable storage media. Further, the program may be transmitted from the network via an electric communication line.
　（２）第１実施形態及び第１変形例～第７変形例の各々は、ＬＴＥ（Long　Term　Evolution）、ＬＴＥ－Ａ（ＬＴＥ－Ａｄｖａｎｃｅｄ）、ＳＵＰＥＲ　３Ｇ、ＩＭＴ－Ａｄｖａｎｃｅｄ、４Ｇ、５Ｇ、ＦＲＡ（Future　Radio　Access）、Ｗ－ＣＤＭＡ（登録商標）、ＧＳＭ（登録商標）、ＣＤＭＡ２０００、ＵＭＢ（Ultra　Mobile　Broadband）、ＩＥＥＥ　８０２．１１（Ｗｉ－Ｆｉ）、ＩＥＥＥ　８０２．１６（ＷｉＭＡＸ）、ＩＥＥＥ　８０２．２０、ＵＷＢ（Ｕｌｔｒａ－ＷｉｄｅＢａｎｄ）、Ｂｌｕｅｔｏｏｔｈ（登録商標）、その他の適切なシステムを利用するシステム及び／又はこれらに基づいて拡張された次世代システムに適用されてもよい。 (2) The first embodiment and each of the first to seventh modifications are LTE (Long Term Evolution), LTE-A (LTE-Advanced), SUPER 3G, IMT-Advanced, 4G, 5G, and FRA ( Future Radio Access), W-CDMA (registered trademark), GSM (registered trademark), CDMA2000, UMB (Ultra Mobile Broadband), IEEE 802.11 (Wi-Fi), IEEE 802.16 (WiMAX), IEEE 802.20. , UWB (Ultra-WideBand), Bluetooth (registered trademark), and other suitable systems, and/or may be applied to a next-generation system extended based on these systems.
　（３）第１実施形態及び第１変形例～第７変形例の各々において説明した情報などは、様々な異なる技術のいずれかを使用して表されてもよい。例えば、上記の説明全体に渡って言及され得るデータ、情報、ビット、チップなどは、電圧、電流、電磁波、磁界、磁性粒子、光場、光子、又はこれらの任意の組み合わせにて表されてもよい。
　なお、本明細書において説明した用語及び／又は本明細書の理解に必要な用語は、同一の又は類似する意味を有する用語と置き換えられてもよい。 (3) The information described in each of the first embodiment and each of the first modification to the seventh modification may be represented using any of various different technologies. For example, data, information, bits, chips, etc. that may be referred to throughout the above description may be represented by voltage, current, electromagnetic waves, magnetic fields, magnetic particles, optical fields, photons, or any combination thereof. Good.
Note that the terms described in the present specification and/or the terms necessary for understanding the present specification may be replaced with terms having the same or similar meanings.
　（４）第１実施形態及び第１変形例～第７変形例の各々において、入出力された情報等は特定の場所（例えば、メモリ）に保存されてもよいし、管理テーブルによって管理されてもよい。入出力される情報等は、上書き、更新、又は追記され得る。出力された情報等は削除されてもよい。入力された情報等は他の装置へ送信されてもよい。 (4) In each of the first embodiment and the first modification to the seventh modification, the input/output information and the like may be stored in a specific location (for example, a memory) or may be managed by a management table. Good. Information that is input/output may be overwritten, updated, or added. The output information and the like may be deleted. The input information and the like may be transmitted to another device.
　（５）第１実施形態及び第１変形例～第７変形例の各々において、判定は、１ビットによって表される値（０か１か）に基づいて行われてもよいし、真偽値（Boolean：true又はfalse）に基づいて行われてもよいし、数値の比較（例えば、所定の値との比較）に基づいて行われてもよい。 (5) In each of the first embodiment and the first to seventh modifications, the determination may be made based on the value (0 or 1) represented by 1 bit, or the true/false value. It may be performed based on (Boolean: true or false) or may be performed based on comparison of numerical values (for example, comparison with a predetermined value).
　（６）第１実施形態及び第１変形例～第７変形例の各々において例示した処理手順、シーケンス、又はフローチャート等は、矛盾のない限り、順序を入れ替えてもよい。例えば、本明細書において説明した方法については、例示的な順序において様々なステップの要素を提示しており、提示した特定の順序に限定されない。 (6) The order of the processing procedures, sequences, flowcharts, etc. illustrated in each of the first embodiment and each of the first modification to the seventh modification may be changed as long as there is no contradiction. For example, the methods described herein present elements of the various steps in a sample order, and are not limited to the specific order presented.
　（７）図１、図２又は図５に例示された各機能は、ハードウェア及びソフトウェアの任意の組み合わせによって実現される。また、各機能は、単体の装置によって実現されてもよいし、相互に別体にて構成された２個以上の装置によって実現されてもよい。 (7) Each function illustrated in FIG. 1, FIG. 2 or FIG. 5 is realized by an arbitrary combination of hardware and software. Further, each function may be realized by a single device, or may be realized by two or more devices that are configured separately from each other.
　（８）第１実施形態及び第１変形例～第７変形例の各々において例示したプログラムは、ソフトウェア、ファームウェア、ミドルウェア、マイクロコード又はハードウェア記述言語と呼ばれるか、他の名称によって呼ばれるかを問わず、命令、命令セット、コード、コードセグメント、プログラムコード、サブプログラム、ソフトウェアモジュール、アプリケーション、ソフトウェアアプリケーション、ソフトウェアパッケージ、ルーチン、サブルーチン、オブジェクト、実行可能ファイル、実行スレッド、手順又は機能等を意味するよう広く解釈されるべきである。
　また、ソフトウェア、又は命令などは、伝送媒体を介して送受信されてもよい。例えば、ソフトウェアが、同軸ケーブル、光ファイバケーブル、ツイストペア及びデジタル加入者回線（ＤＳＬ）などの有線技術及び／又は赤外線、無線及びマイクロ波などの無線技術を使用してウェブサイト、サーバ、又は他のリモートソースから送信される場合、これらの有線技術及び／又は無線技術は、伝送媒体の定義内に含まれる。 (8) Whether the program illustrated in each of the first embodiment and the first modification to the seventh modification is called software, firmware, middleware, microcode, a hardware description language, or another name. , Instruction, instruction set, code, code segment, program code, subprogram, software module, application, software application, software package, routine, subroutine, object, executable file, execution thread, procedure or function, etc. It should be widely interpreted.
In addition, software, instructions, and the like may be transmitted and received via a transmission medium. For example, the software may use a wired technology such as coaxial cable, fiber optic cable, twisted pair and digital subscriber line (DSL) and/or wireless technology such as infrared, wireless and microwave to websites, servers, or other When transmitted from a remote source, these wireline and/or wireless technologies are included within the definition of transmission medium.
　（９）第１実施形態及び第１変形例～第７変形例の各々において、「システム」及び「ネットワーク」という用語は、互換的に使用される。 (9) In each of the first embodiment and the first to seventh modifications, the terms “system” and “network” are used interchangeably.
　（１０）第１実施形態及び第１変形例～第７変形例の各々において、情報処理装置２０及び４１～４ｎは、移動局でもよい。移動局は、当業者によって、加入者局、モバイルユニット、加入者ユニット、ワイヤレスユニット、リモートユニット、モバイルデバイス、ワイヤレスデバイス、ワイヤレス通信デバイス、リモートデバイス、モバイル加入者局、アクセス端末、モバイル端末、ワイヤレス端末、リモート端末、ハンドセット、ユーザエージェント、モバイルクライアント、クライアント、又はいくつかの他の適切な用語を用いて称される場合もある。 (10) In each of the first embodiment and the first to seventh modifications, the information processing devices 20 and 41 to 4n may be mobile stations. Mobile stations are defined by those skilled in the art as subscriber stations, mobile units, subscriber units, wireless units, remote units, mobile devices, wireless devices, wireless communication devices, remote devices, mobile subscriber stations, access terminals, mobile terminals, wireless. It may also be referred to as a terminal, remote terminal, handset, user agent, mobile client, client, or some other suitable term.
　（１１）第１実施形態及び第１変形例～第６変形例の各々において、「に基づいて」という記載は、別段に明記されていない限り、「のみに基づいて」を意味しない。言い換えれば、「に基づいて」という記載は、「のみに基づいて」と「に少なくとも基づいて」の両方を意味する。 (11) In each of the first embodiment and the first to sixth modifications, the description “based on” does not mean “based only on” unless otherwise specified. In other words, the phrase "based on" means both "based only on" and "based at least on."
　（１２）本明細書において使用する「第１」及び「第２」などの呼称を使用した要素へのいかなる参照も、それらの要素の量又は順序を全般的に限定しない。これらの呼称は、２つ以上の要素間を区別する便利な方法として本明細書において使用され得る。したがって、第１及び第２の要素への参照は、２つの要素のみが採用され得ること又は何らかの形において第１要素が第２要素に先行しなければならないことを意味しない。 (12) Any reference to elements using designations such as “first” and “second” as used herein does not generally limit the amount or order of those elements. These nomenclatures may be used herein as a convenient way to distinguish between two or more elements. Thus, references to the first and second elements do not mean that only two elements can be employed or that the first element must precede the second element in any way.
　（１３）第１実施形態及び第１変形例～第６変形例の各々において「含む（ｉｎｃｌｕｄｉｎｇ）」、「含んでいる（ｃｏｍｐｒｉｓｉｎｇ）」、及びそれらの変形が、本明細書あるいは特許請求の範囲において使用されている限り、これら用語は、用語「備える」と同様に、包括的であることが意図される。さらに、本明細書あるいは特許請求の範囲において使用されている用語「又は（or）」は、排他的論理和ではないことが意図される。 (13) In each of the first embodiment and the first modification to the sixth modification, the terms “including”, “comprising”, and their variations are the present specification and claims. As used in, the terms as well as the term "comprising" are intended to be inclusive. Further, the term "or" as used in the specification or claims is not intended to be an exclusive OR.
　（１４）本願の全体において、例えば、英語におけるa、an及びtheのように、翻訳によって冠詞が追加された場合、これらの冠詞は、文脈から明らかにそうではないことが示されていなければ、複数を含む。 (14) Throughout the application, if translations add articles, such as a, an, and the in English, unless these articles clearly indicate otherwise, Including multiple.
　（１５）本発明が本明細書中に説明した実施形態に限定されないことは当業者にとって明白である。本発明は、特許請求の範囲の記載に基づいて定まる本発明の趣旨及び範囲を逸脱することなく修正及び変更態様として実施できる。従って、本明細書の記載は、例示的な説明を目的とし、本発明に対して何ら制限的な意味を有さない。また、本明細書に例示した態様から選択された複数の態様を組み合わせてもよい。 (15) It will be apparent to those skilled in the art that the present invention is not limited to the embodiments described herein. The present invention can be implemented as modified and changed modes without departing from the spirit and scope of the present invention defined based on the description of the claims. Therefore, the description of the present specification is for the purpose of exemplifying description, and has no restrictive meaning to the present invention. In addition, a plurality of modes selected from the modes exemplified in this specification may be combined.
　１…評価システム、１０…配信サーバ、１０１…通信装置、１０２…記憶装置、１０３…処理装置、１１０…配信制御部、２０…情報処理装置、２０１…入力装置、２０２…表示装置、２０３…スピーカ、２０４…通信装置、２０５…記憶装置、２０６…処理装置、２１０…再生実行部、２２０…動作制御部、３０…評価装置、３０１…通信装置、３０２…表示装置、３０３…スピーカ、３０４…記憶装置、３０５…処理装置、３１０…受取部、３２０…評価部、３３０…出力部、４１－４ｎ…情報処理装置、５０…学習モデル生成装置、５０１…入力装置、５０２…表示装置、５０３…スピーカ、５０４…通信装置、５０５…記憶装置、５０６…処理装置、５１０…受取部、５２０…再現部、５３０…評価入手部、５４０…教師データ生成部、５５０…学習モデル生成部。 1... Evaluation system, 10... Delivery server, 101... Communication device, 102... Storage device, 103... Processing device, 110... Delivery control unit, 20... Information processing device, 201... Input device, 202... Display device, 203... Speaker , 204... Communication device, 205... Storage device, 206... Processing device, 210... Replay execution unit, 220... Operation control unit, 30... Evaluation device, 301... Communication device, 302... Display device, 303... Speaker, 304... Storage Device, 305... Processing device, 310... Receiving part, 320... Evaluation part, 330... Output part, 41-4n... Information processing device, 50... Learning model generating device, 501... Input device, 502... Display device, 503... Speaker , 504... Communication device, 505... Storage device, 506... Processing device, 510... Receiving unit, 520... Reproducing unit, 530... Evaluation obtaining unit, 540... Teacher data generating unit, 550... Learning model generating unit.

Claims (7)

  1. 　動画の再生に関する状況と、前記状況に対する評価と、の関係を示す関係データを学習した学習モデルを用いて、動画再生用ソフトウェアが行う動画の再生に関する再生状況を評価する評価部と、
    　前記評価部での評価結果に基づく情報を出力する出力部と、
    　を含む評価装置。     Using a learning model that has learned relational data indicating the relationship between the situation regarding the reproduction of the moving image and the evaluation, the evaluation unit that evaluates the reproduction situation regarding the reproduction of the moving image performed by the software for reproducing the moving image,
    An output unit that outputs information based on the evaluation result of the evaluation unit,
    Evaluation device including.
  2. 　前記再生状況を示す状況情報を受け取る受取部をさらに含み、
    　前記評価部は、前記受取部が受け取る前記状況情報が示す前記再生状況に応じた前記評価結果を生成する、
    　請求項１に記載の評価装置。     Further comprising a receiver for receiving status information indicating the playback status,
    The evaluation unit generates the evaluation result according to the reproduction status indicated by the status information received by the reception unit,
    The evaluation device according to claim 1.
  3. 　前記動画の再生に関する前記状況は、
    　前記動画を構成する連続する画像フレームのうち再生されなかった画像フレームの数の状況、及び、
    　前記動画と共に再生されるべき音声と前記動画との再生タイミングのズレの状況
    　の両方又は一方を含む、
    　請求項１又は２に記載の評価装置。     The situation regarding the playback of the video is
    A situation of the number of image frames that have not been reproduced among the consecutive image frames that form the moving image, and
    Including both or one of the audio to be played together with the video and the situation of the timing difference between the playback timing of the video,
    The evaluation device according to claim 1.
  4. 　前記動画の再生に関する前記状況は、さらに、
    　前記動画を再生する再生装置の負荷の状況、及び、
    　前記動画を示す動画データのビットレートの状況
    　の両方又は一方を含む、
    　請求項３に記載の評価装置。     The situation regarding the reproduction of the video is
    The load status of the playback device that plays back the video, and
    Including both or one of the bit rate situations of the video data showing the video
    The evaluation device according to claim 3.
  5. 　前記動画データは、エンコードされており、
    　前記再生装置の負荷の状況は、
    　前記再生装置が前記動画データをデコードするために要する時間を示す、
    　請求項４に記載の評価装置。     The video data is encoded,
    The load condition of the playback device is
    Indicates the time required for the playback device to decode the video data,
    The evaluation device according to claim 4.
  6. 　前記動画データは、暗号化されており、
    　前記再生装置の負荷の状況は、
    　前記再生装置が前記動画データをデクリプトするために要する時間を示す、
    　請求項４又は５に記載の評価装置。     The moving image data is encrypted,
    The load condition of the playback device is
    Indicating the time required for the playback device to decrypt the video data,
    The evaluation device according to claim 4 or 5.
  7. 　前記関係データを用いて、前記学習モデルを生成する学習モデル生成部をさらに含む、
    　請求項１から６のいずれか１項に記載の評価装置。     Further including a learning model generation unit that generates the learning model using the relational data,
    The evaluation device according to any one of claims 1 to 6.
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JPH1169325A (en) * 1997-08-11 1999-03-09 Ando Electric Co Ltd Dynamic image communication management device
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