CN116095414A - Method and device for acquiring video screenshot - Google Patents
Method and device for acquiring video screenshot Download PDFInfo
- Publication number
- CN116095414A CN116095414A CN202211249420.4A CN202211249420A CN116095414A CN 116095414 A CN116095414 A CN 116095414A CN 202211249420 A CN202211249420 A CN 202211249420A CN 116095414 A CN116095414 A CN 116095414A
- Authority
- CN
- China
- Prior art keywords
- screenshot
- video sequence
- load
- video
- sample
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
- 238000000034 method Methods 0.000 title claims abstract description 52
- 230000004044 response Effects 0.000 claims abstract description 16
- 238000004364 calculation method Methods 0.000 claims description 103
- 238000007405 data analysis Methods 0.000 claims description 11
- 238000004590 computer program Methods 0.000 claims description 9
- 238000007619 statistical method Methods 0.000 claims description 8
- 238000012545 processing Methods 0.000 description 12
- 238000010586 diagram Methods 0.000 description 10
- 230000008569 process Effects 0.000 description 8
- 238000004891 communication Methods 0.000 description 7
- 230000006870 function Effects 0.000 description 6
- 230000003287 optical effect Effects 0.000 description 3
- 238000004458 analytical method Methods 0.000 description 2
- 230000008713 feedback mechanism Effects 0.000 description 2
- 239000000835 fiber Substances 0.000 description 2
- 230000000644 propagated effect Effects 0.000 description 2
- 238000005070 sampling Methods 0.000 description 2
- 238000012360 testing method Methods 0.000 description 2
- 238000000540 analysis of variance Methods 0.000 description 1
- 230000005540 biological transmission Effects 0.000 description 1
- 238000005516 engineering process Methods 0.000 description 1
- 239000004973 liquid crystal related substance Substances 0.000 description 1
- 238000005259 measurement Methods 0.000 description 1
- 239000013307 optical fiber Substances 0.000 description 1
- 239000004065 semiconductor Substances 0.000 description 1
- 239000002699 waste material Substances 0.000 description 1
Images
Classifications
-
- 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]
- H04N21/40—Client devices specifically adapted for the reception of or interaction with content, e.g. set-top-box [STB]; Operations thereof
- H04N21/47—End-user applications
- H04N21/472—End-user interface for requesting content, additional data or services; End-user interface for interacting with content, e.g. for content reservation or setting reminders, for requesting event notification, for manipulating displayed content
- H04N21/47205—End-user interface for requesting content, additional data or services; End-user interface for interacting with content, e.g. for content reservation or setting reminders, for requesting event notification, for manipulating displayed content for manipulating displayed content, e.g. interacting with MPEG-4 objects, editing locally
-
- 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]
- H04N21/40—Client devices specifically adapted for the reception of or interaction with content, e.g. set-top-box [STB]; Operations thereof
- H04N21/43—Processing of content or additional data, e.g. demultiplexing additional data from a digital video stream; Elementary client operations, e.g. monitoring of home network or synchronising decoder's clock; Client middleware
- H04N21/44—Processing of video elementary streams, e.g. splicing a video clip retrieved from local storage with an incoming video stream or rendering scenes according to encoded video stream scene graphs
- H04N21/44008—Processing of video elementary streams, e.g. splicing a video clip retrieved from local storage with an incoming video stream or rendering scenes according to encoded video stream scene graphs involving operations for analysing video streams, e.g. detecting features or characteristics in the video stream
Landscapes
- Engineering & Computer Science (AREA)
- Multimedia (AREA)
- Signal Processing (AREA)
- Databases & Information Systems (AREA)
- Human Computer Interaction (AREA)
- Closed-Circuit Television Systems (AREA)
- Two-Way Televisions, Distribution Of Moving Picture Or The Like (AREA)
Abstract
The embodiment of the disclosure provides a method and a device for acquiring video screenshot. The method for acquiring the video screenshot comprises the following steps: firstly, in response to obtaining a video sequence set for executing a screenshot task, obtaining video information of a plurality of candidate video sequences in the video sequence set, then, based on the video information of the plurality of candidate video sequences, calculating expected screenshot loads corresponding to the plurality of candidate video sequences, selecting a target video sequence from the video sequence set based on local available resources and the expected screenshot loads corresponding to the plurality of candidate video sequences, and finally, executing a screenshot task of the target video sequence to obtain a video screenshot corresponding to the target video sequence, wherein the expected screenshot load corresponding to the screenshot task of each video sequence can be predetermined before executing the screenshot task, the number of the screenshot tasks can be accurately allocated in advance, and load balance and resource utilization rate are improved.
Description
Technical Field
The embodiment of the disclosure relates to the technical field of computers and the technical field of Internet, in particular to the technical field of video processing and the technical field of image processing, and particularly relates to a method and a device for acquiring video screenshot.
Background
As video on demand continues to evolve, more and more users are in need of on-demand screenshots. The existing CPU intensive tasks such as video-on-demand screenshot and the like have the following processing flows: and configuring a default thread number for each screenshot task, and determining the concurrency number of the tasks on the node according to the overall core number configuration of the machine.
The whole processing process of the screenshot task can be divided into the steps of downloading media resources, decoding video parts in the media resources, encoding pictures, uploading picture finished products and the like. In the steps, the downloading of media resources consumes the downlink bandwidth of the system, the video decoding and the picture encoding mainly consume the cpu load of the system, and the uploading of pictures mainly consumes the uplink bandwidth of the system. The concurrent number of downloads and uploads is limited by the bandwidth limitations of the overall network in which the server is located, which generally does not first become a bottleneck, and is more relaxed than the parallel constraints of video decoding and picture encoding. The decoding consumption of different media resources is different, and all screenshot tasks are processed by using the same thread number, so that the processing speed of the tasks, especially the screenshot speed of the tasks with higher video complexity is lower, and the waste of the whole system resources is caused.
Disclosure of Invention
Embodiments of the present disclosure provide a method, apparatus, electronic device, and computer-readable medium for obtaining a video screenshot.
In a first aspect, embodiments of the present disclosure provide a method of obtaining a video screenshot, the method comprising: in response to acquiring a set of video sequences for performing a screenshot task, acquiring video information of a plurality of candidate video sequences in the set of video sequences; calculating expected screenshot loads corresponding to the candidate video sequences based on the video information of the candidate video sequences; selecting a target video sequence from the video sequence set based on locally available resources and predicted screenshot loads corresponding to the plurality of candidate video sequences; and executing the screenshot task of the target video sequence to obtain the video screenshot corresponding to the target video sequence.
In some embodiments, calculating the predicted screenshot load for the plurality of candidate video sequences based on video information for the plurality of candidate video sequences includes: acquiring a load calculation formula for calculating a screenshot load; and calculating the expected screenshot loads corresponding to the candidate video sequences based on the load calculation formula and the video information of the candidate video sequences.
In some embodiments, obtaining a load calculation formula for calculating a screenshot load includes: acquiring a sample video sequence set, wherein the sample video sequence set comprises a plurality of sample video sequences, sample video information of the plurality of sample video sequences and sample screenshot loads corresponding to the plurality of sample video sequences; and carrying out data analysis on the plurality of sample video sequences, the sample video information of the plurality of sample video sequences and the sample screenshot loads corresponding to the plurality of sample video sequences by using a regression statistical method, and obtaining a load calculation formula related to the sample video information and the sample screenshot loads.
In some embodiments, the method further comprises: responding to the screenshot task of the target video sequence, and acquiring a real-time screenshot load corresponding to the target video sequence; and updating the load calculation formula based on the real-time screenshot load corresponding to the target video sequence and the expected screenshot load corresponding to the target video sequence to obtain a new load calculation formula.
In some embodiments, updating the load calculation formula based on the real-time screenshot load corresponding to the target video sequence and the expected screenshot load corresponding to the target video sequence to obtain a new load calculation formula, including: comparing the real-time screenshot load corresponding to the target video sequence with the predicted screenshot load corresponding to the target video sequence, and judging whether the real-time screenshot load corresponding to the target video sequence meets the load condition or not; in response to determining that the real-time screenshot load corresponding to the target video sequence meets a load condition, storing the real-time screenshot load corresponding to the target video sequence and video information of the target video sequence into a sample video sequence set to obtain a new sample video sequence set; and updating the load calculation formula based on the new sample video sequence set to obtain a new load calculation formula.
In some embodiments, the method further comprises: calculating a new expected screenshot load corresponding to the target video sequence based on a new load calculation formula and video information of the target video sequence; and executing preset adjustment operation on the target video sequence according to the locally available resources and the new expected screenshot load corresponding to the target video sequence.
In a second aspect, embodiments of the present disclosure provide an apparatus for obtaining a video screenshot, the apparatus comprising: an acquisition module configured to acquire video information of a plurality of candidate video sequences in a set of video sequences in response to acquiring the set of video sequences for performing the screenshot task; a calculation module configured to calculate predicted screenshot loads corresponding to the plurality of candidate video sequences based on video information of the plurality of candidate video sequences; the selecting module is configured to select a target video sequence from the video sequence set based on the locally available resources and the expected screenshot loads corresponding to the plurality of candidate video sequences; and the execution module is configured to execute the screenshot task of the target video sequence to obtain the video screenshot corresponding to the target video sequence.
In some embodiments, the computing module includes: an acquisition unit configured to acquire a load calculation formula for calculating a screenshot load; and a calculating unit configured to calculate expected screenshot loads corresponding to the plurality of candidate video sequences based on the load calculation formula and video information of the plurality of candidate video sequences.
In some embodiments, the computing unit is further configured to: acquiring a sample video sequence set, wherein the sample video sequence set comprises a plurality of sample video sequences, sample video information of the plurality of sample video sequences and sample screenshot loads corresponding to the plurality of sample video sequences; and carrying out data analysis on the plurality of sample video sequences, the sample video information of the plurality of sample video sequences and the sample screenshot loads corresponding to the plurality of sample video sequences by using a regression statistical method, and obtaining a load calculation formula related to the sample video information and the sample screenshot loads.
In some embodiments, the apparatus further comprises an update module; an acquisition module further configured to: responding to the screenshot task of the target video sequence, and acquiring a real-time screenshot load corresponding to the target video sequence; an update module configured to: and updating the load calculation formula based on the real-time screenshot load corresponding to the target video sequence and the expected screenshot load corresponding to the target video sequence to obtain a new load calculation formula.
In some embodiments, the update module is further configured to: comparing the real-time screenshot load corresponding to the target video sequence with the predicted screenshot load corresponding to the target video sequence, and judging whether the real-time screenshot load corresponding to the target video sequence meets the load condition or not; in response to determining that the real-time screenshot load corresponding to the target video sequence meets a load condition, storing the real-time screenshot load corresponding to the target video sequence and video information of the target video sequence into a sample video sequence set to obtain a new sample video sequence set; and updating the load calculation formula based on the new sample video sequence set to obtain a new load calculation formula.
In some embodiments, the computing module is further configured to: calculating a new expected screenshot load corresponding to the target video sequence based on a new load calculation formula and video information of the target video sequence; an execution module further configured to: and executing preset adjustment operation on the target video sequence according to the locally available resources and the new expected screenshot load corresponding to the target video sequence.
In a third aspect, embodiments of the present disclosure provide an electronic device comprising: one or more processors; a storage device having one or more programs stored thereon; the one or more programs, when executed by the one or more processors, cause the one or more processors to implement a method of obtaining a video screenshot as described in any one of the embodiments of the first aspect.
In a fourth aspect, embodiments of the present disclosure provide a computer readable medium having stored thereon a computer program which, when executed by a processor, implements a method of obtaining a video screenshot as described in any one of the embodiments of the first aspect.
According to the method for acquiring the video screenshot provided by the embodiment of the disclosure, the execution main body firstly responds to the acquired video sequence set for executing the screenshot task, acquires video information of a plurality of candidate video sequences in the video sequence set, then calculates expected screenshot loads corresponding to the plurality of candidate video sequences based on the video information of the plurality of candidate video sequences, selects a target video sequence from the video sequence set based on local available resources and the expected screenshot loads corresponding to the plurality of candidate video sequences, finally executes the screenshot task of the target video sequence to acquire the video screenshot corresponding to the target video sequence, can pre-determine expected screenshot loads corresponding to the screenshot task of each video sequence before executing the screenshot task, can pre-accurately allocate the quantity of the screenshot tasks, does not need to limit machine resources which can be used by the task by setting the line-to-line number of the task, can allocate resource quota for the screenshot task based on the expected screenshot loads which are accurately calculated, can allocate more tasks based on the expected screenshot loads, fully utilizes the local available resources of a server, and improves the load balance and the resource utilization rate.
Drawings
Other features, objects and advantages of the present disclosure will become more apparent upon reading of the detailed description of non-limiting embodiments, made with reference to the following drawings:
FIG. 1 is an exemplary system architecture diagram in which an embodiment of the present disclosure may be applied;
FIG. 2 is a flow chart of one embodiment of a method of obtaining a video screenshot in accordance with the present disclosure;
FIG. 3 is a schematic diagram of one application scenario of a method of obtaining a video screenshot according to the present disclosure;
FIG. 4 is a flow chart of one embodiment of calculating expected screenshot loads for a plurality of candidate video sequences according to the present disclosure;
FIG. 5 is a flow chart of one embodiment of obtaining a load calculation formula according to the present disclosure;
FIG. 6 is a flow chart of another embodiment of a method of obtaining a video screenshot in accordance with the present disclosure;
FIG. 7 is a flow chart of one embodiment of updating a load calculation formula according to the present disclosure;
FIG. 8 is a schematic structural diagram of one embodiment of an apparatus to obtain a video screenshot in accordance with the present disclosure;
fig. 9 is a schematic structural diagram of an electronic device suitable for use in implementing embodiments of the present disclosure.
Detailed Description
The present disclosure is described in further detail below with reference to the drawings and examples. It is to be understood that the specific embodiments described herein are merely illustrative of the related disclosure and not limiting thereof. It should be further noted that, for convenience of description, only the portions related to the disclosure are shown in the drawings.
It should be noted that, without conflict, the embodiments of the present disclosure and features of the embodiments may be combined with each other. The present disclosure will be described in detail below with reference to the accompanying drawings in conjunction with embodiments.
FIG. 1 illustrates an exemplary system architecture 100 in which the methods and apparatus of obtaining video shots of embodiments of the present disclosure may be applied.
As shown in fig. 1, the system architecture 100 may include terminal devices 104, 105, 106, a network 107, servers 101, 102, 103. The network 107 is the medium used to provide communication links between the terminal devices 104, 105, 106 and the servers 101, 102, 103. The network 107 may include various connection types, such as wired, wireless communication links, or fiber optic cables, among others.
A user may interact with servers 101, 102, 103 belonging to the same server cluster via a network 107 via terminal devices 104, 105, 106 to receive or send information etc. Various applications may be installed on the terminal devices 104, 105, 106, such as an item display application, a data analysis application, a search class application, etc.
The terminal devices 104, 105, 106 may be hardware or software. When the terminal device is hardware, it may be a variety of electronic devices having a display screen and supporting communication with a server, including but not limited to smartphones, tablets, laptop and desktop computers, and the like. When the terminal device is software, it can be installed in the above-listed electronic device. Which may be implemented as a plurality of software or software modules, or as a single software or software module. The present invention is not particularly limited herein.
The servers 101, 102, 103 may be servers providing various services, such as a background server that receives a request transmitted from a terminal device with which a communication connection is established. The background server can receive and analyze the request sent by the terminal equipment and generate a processing result.
The servers 101, 102, 103 may obtain a video sequence set for executing a screenshot task, analyze and process a plurality of candidate video sequences in the video sequence set, obtain video information of the plurality of candidate video sequences in the video sequence set, calculate predicted screenshot loads corresponding to the plurality of candidate video sequences based on the video information of the plurality of candidate video sequences, select a target video sequence from the video sequence set based on local available resources and the predicted screenshot loads corresponding to the plurality of candidate video sequences, and finally execute the screenshot task of the target video sequence to obtain a video screenshot corresponding to the target video sequence.
The server may be hardware or software. When the server is hardware, it may be various electronic devices that provide various services to the terminal device. When the server is software, a plurality of software or software modules providing various services to the terminal device may be realized, or a single software or software module providing various services to the terminal device may be realized. The present invention is not particularly limited herein.
It should be noted that the method for obtaining the video screenshot provided by the embodiments of the present disclosure may be performed by the servers 101, 102, 103. Accordingly, the means for obtaining the video shots are provided in the servers 101, 102, 103.
It should be understood that the number of terminal devices, networks and servers in fig. 1 is merely illustrative. There may be any number of terminal devices, networks, and servers, as desired for implementation.
With continued reference to fig. 2, a flow 200 of one embodiment of a method of obtaining a video screenshot in accordance with the present disclosure is shown. The method for acquiring the video screenshot comprises the following steps:
in response to obtaining a set of video sequences for performing the screenshot task, video information for a plurality of candidate video sequences in the set of video sequences is obtained, step 210.
In this step, the executing body (for example, servers 101, 102, 103 in fig. 1) on which the method for obtaining the video screenshot operates may send or read, through the receiving terminal, a set of video sequences composed of a plurality of candidate video sequences, where each candidate video sequence is used to perform a screenshot task to obtain a corresponding video screenshot, and each candidate video sequence is a sequence in which a plurality of video images with time-space association are arranged in a certain order.
The executing body may perform video parsing processing on each candidate video sequence in the video sequence set, detect media information of each candidate video sequence, and obtain video information of each candidate video sequence, where the video information characterizes video complexity information of the candidate video sequence, and may include information such as resolution, code rate, frame rate, coding format, and the like of the candidate video sequence, where video information of different candidate video sequences may be different. Where resolution is a parameter used to measure how much data is in an image, usually expressed as ppi (Pixel per inch), for example, a resolution of 320×180 for a certain video sequence, which means that its effective pixels in the lateral and longitudinal directions have higher ppi values for the window hours and appear clear, and when the window is enlarged, the effective pixels have a reduced ppi value because less effective pixels fill the window and are blurred; the code rate is the number of data bits transmitted in unit time during data transmission, the unit is kbps, namely kilobits per second, namely sampling rate, the greater the sampling rate in unit time is, the higher the precision is, and the processed file is more similar to the original file; the frame rate is a measure for measuring the number of display frames. The measurement units are display frames per second (Frames per Second, FPS) or "hertz" (Hz), and the Frames Per Second (FPS) or frame rate represents the number of times the graphic processor can update per second when processing fields, and the high frame rate can obtain smoother and more realistic animation; the encoding format is the existence form of video files, and may include formats of H264, VP8, AVS, RMVB, WMV, quickTime (mov), H265, VP9, AV1, and the like.
In this step, because different video information may cause different loads of the video sequences used in the screenshot task, the more complex video sequences of the video information use higher loads in the screenshot task, after the execution subject obtains the video information of the plurality of candidate video sequences, load calculation may be performed on each candidate video sequence according to the video information of each candidate video sequence and the screenshot task corresponding to each candidate video sequence, so as to obtain a predicted screenshot load corresponding to each candidate video sequence when aiming at the screenshot task, where the predicted screenshot load may represent a load value possibly required by the candidate video sequence in the process of executing the screenshot task.
The execution body may perform association marking on the predicted screenshot load corresponding to each candidate video sequence and the candidate video sequences, so as to obtain predicted screenshot loads corresponding to a plurality of candidate video sequences.
At step 230, a target video sequence is selected from the set of video sequences based on locally available resources and predicted screenshot loads corresponding to the plurality of candidate video sequences.
In this step, after the execution body obtains the expected screenshot loads corresponding to the multiple candidate video sequences, the execution body may obtain a locally available resource, where the locally available resource may represent a resource value that is locally used by the screenshot task, for example, a value of a cpu that may be used.
The execution body may determine, according to the locally available resources and the predicted screenshot loads corresponding to the plurality of candidate video sequences, how many candidate video sequences can be provided with the locally available resources by using the locally available resources, that is, may calculate, from the predicted screenshot loads corresponding to the plurality of candidate video sequences, candidate video sequences whose predicted screenshot load addition is not greater than the locally available resources, determine the determined candidate video sequences as target video sequences, so that a target video sequence whose predicted screenshot load addition is not greater than the locally available resources may be selected from the set of video sequences, and the sum of the predicted screenshot loads is as equal as possible to the locally available resources, where the target video sequence may include one or more video sequences, which is not specifically limited.
As an example, the value of the locally available resource is 100, the plurality of candidate video sequences include candidate video sequence a, candidate video sequence B, candidate video sequence C, candidate video sequence D and candidate video sequence E, the expected screenshot load 30 corresponding to candidate video sequence a, the expected screenshot load 50 corresponding to candidate video sequence B, the expected screenshot load 60 corresponding to candidate video sequence C, the expected screenshot load 70 corresponding to candidate video sequence D, and the expected screenshot load 60 corresponding to candidate video sequence E.
And 240, executing a screenshot task of the target video sequence to obtain a video screenshot corresponding to the target video sequence.
In this step, after the execution body selects the target video sequence, the execution body may start to execute the screenshot task of the target video sequence, and start to execute the steps of downloading media resources, decoding video portions in the media resources, encoding pictures, uploading finished pictures, and the like on the target video sequence, so as to obtain the video screenshot corresponding to the target video sequence.
If the target video sequence includes a plurality of video sequences, the execution body may execute steps of media resource downloading, video part decoding in the media resource, picture encoding, uploading a picture finished product, and the like on each video sequence, so as to obtain a video screenshot corresponding to each video sequence.
With continued reference to fig. 3, fig. 3 is a schematic diagram of an application scenario of the method for acquiring a video screenshot according to the present embodiment. The method may be applied in the application scenario of fig. 3, and the terminal 301 may send a set of video sequences for performing the screenshot task to the server 302. After receiving the video sequence set, the server 302 may perform video analysis on the multiple candidate video sequences to obtain video information of the multiple candidate video sequences in the video sequence set, and then the server 302 calculates expected screenshot loads corresponding to the multiple candidate video sequences based on the video information of the multiple candidate video sequences. The server 302 may then select a target video sequence from the set of video sequences based on the locally available resources and the predicted screenshot loads corresponding to the plurality of candidate video sequences, and begin executing the screenshot tasks of the target video sequence to obtain a video screenshot corresponding to the target video sequence. Server 302 sends the video clip to terminal 301 and terminal 301 may present the video clip to the user via a screen.
According to the method for acquiring the video screenshot provided by the embodiment of the disclosure, the execution main body firstly responds to the acquired video sequence set for executing the screenshot task, acquires video information of a plurality of candidate video sequences in the video sequence set, then calculates expected screenshot loads corresponding to the plurality of candidate video sequences based on the video information of the plurality of candidate video sequences, selects a target video sequence from the video sequence set based on local available resources and the expected screenshot loads corresponding to the plurality of candidate video sequences, finally executes the screenshot task of the target video sequence to acquire the video screenshot corresponding to the target video sequence, can pre-determine expected screenshot loads corresponding to the screenshot task of each video sequence before executing the screenshot task, can pre-accurately allocate the quantity of the screenshot tasks, does not need to limit machine resources which can be used by the task by setting the line-to-line number of the task, can allocate resource quota for the screenshot task based on the expected screenshot loads which are accurately calculated, can allocate more tasks based on the expected screenshot loads, fully utilizes the local available resources of a server, and improves the load balance and the resource utilization rate.
Referring to fig. 4, fig. 4 shows a flowchart 400 of one embodiment of calculating predicted screenshot loads corresponding to a plurality of candidate video sequences, that is, step 220 described above, based on video information of the plurality of candidate video sequences, the calculating of the predicted screenshot loads corresponding to the plurality of candidate video sequences may include the steps of:
In this step, after the executing body acquires the video information of the plurality of candidate video sequences, the executing body may locally read a load calculation formula for calculating the screenshot load, where the load calculation formula is in a positive correlation with the video information of the video sequences. The load calculation formula may be expressed as (x) video information, where (x) may be a calculation parameter, and may be a parameter obtained by fitting a plurality of test samples.
Wherein, the video information may include resolution, code rate, frame rate and coding format of the video sequence, the load calculation formula may be expressed as follows: the screenshot load = (x 1) resolution + (x 2) code rate + (x 3) frame rate + (x 4) code format is expected.
In this step, after the executing body obtains the load calculation formula and the video information of the plurality of candidate video sequences, the executing body may respectively bring the video information of each candidate video sequence into the load calculation formula, and calculate the expected screenshot load corresponding to each candidate video sequence.
As an example, the executing body may obtain the video information of the candidate video sequence a including the resolution a1, the code rate a2, the frame rate a3 and the coding format a4, and the video information of the candidate video sequence B including the resolution B1, the code rate B2, the frame rate B3 and the coding format B4, and the executing body may bring the resolution a1, the code rate a2, the frame rate a3 and the coding format a4 of the candidate video sequence a into the load calculation formula to obtain the predicted screenshot load= (x 1) ×a1+ (x 2) ×a2+ (x 3) ×a3+ (x 4) a4 of the candidate video sequence a, and may also bring the resolution B1, the code rate B2, the frame rate B3 and the coding format B4 of the candidate video sequence B into the load calculation formula to obtain the predicted screenshot load= (x 1) ×b1+ (x 3) ×b2+ (x 4) of the candidate video sequence B.
In the implementation manner, the expected screenshot load corresponding to a plurality of candidate video sequences is calculated by using a load calculation formula which is in positive correlation calculation relation with the video information of the video sequences, so that the expected screenshot load can be calculated more accurately, the pre-estimation of the screenshot load is realized, and the load balance and the resource utilization rate of screenshot tasks can be improved.
Referring to fig. 5, fig. 5 shows a flowchart 500 of one embodiment of obtaining a load calculation formula, i.e., step 410 described above, for obtaining a load calculation formula for calculating a screenshot load, which may include the steps of:
In this step, the executing body may obtain a plurality of sample video sequences in advance, perform video analysis processing on each sample video sequence, detect media information of each sample video sequence, obtain sample video information of each sample video sequence, and characterize video complexity information of the sample video sequence, where the video complexity information may include information such as resolution, code rate, frame rate, and coding format of the sample video sequence. The execution body may further obtain a sample screenshot load corresponding to each sample video sequence, where the sample screenshot load may be a screenshot load corresponding to when each sample video sequence targets a screenshot task. The execution body may combine the plurality of sample video sequences, the sample video information of the plurality of sample video sequences, and the sample screenshot load corresponding to the plurality of sample video sequences into a sample video sequence set.
And step 520, performing data analysis on the plurality of sample video sequences, the sample video information of the plurality of sample video sequences and the sample screenshot loads corresponding to the plurality of sample video sequences by using a regression statistical method, and obtaining a load calculation formula associated with the sample video information and the sample screenshot loads.
In this step, after the executing body obtains the sample video sequence set, data analysis may be performed on the plurality of sample video sequences, sample video information of the plurality of sample video sequences, and sample screenshot loads corresponding to the plurality of sample video sequences, respectively, and data fitting may be performed on the plurality of sample video sequences, sample video information of the plurality of sample video sequences, and sample screenshot loads corresponding to the plurality of sample video sequences by using a regression statistical method in the data analysis method, so as to obtain a load calculation formula associated with the sample video information and the sample screenshot loads.
In the implementation manner, the load calculation formula associated with the sample video information and the sample screenshot load is obtained through the sample video sequence set, the load calculation formula can be obtained through fitting according to the current test sample, the expected screenshot load can be calculated more accurately by utilizing the load calculation formula, the pre-estimation of the screenshot load is realized, and therefore the load balance and the resource utilization rate of the screenshot task can be improved.
Referring to fig. 6, fig. 6 shows a flowchart 600 of another embodiment of a method of obtaining a video screenshot, which may include the steps of:
in step 610, in response to obtaining a set of video sequences for performing a screenshot task, video information for a plurality of candidate video sequences in the set of video sequences is obtained.
Step 610 of this embodiment may be performed in a similar manner to step 210 of the embodiment shown in fig. 2, and is not repeated here.
Step 620 of this embodiment may be performed in a similar manner to step 220 of the embodiment shown in fig. 2, and is not repeated here.
Step 630 of this embodiment may be performed in a similar manner to step 230 of the embodiment shown in fig. 2, and is not repeated here.
And step 640, executing a screenshot task of the target video sequence to obtain a video screenshot corresponding to the target video sequence.
Step 640 of this embodiment may be performed in a similar manner to step 240 of the embodiment shown in fig. 2, and is not repeated here.
In step 650, in response to executing the screenshot task of the target video sequence, a real-time screenshot load corresponding to the target video sequence is obtained.
In this step, when the execution body starts to execute the screenshot task of the target video sequence, the execution body may also start a timer corresponding to the screenshot task, read a load value in the screenshot task execution process at intervals of a preset time, calculate an accumulated average value occupied by the screenshot task process according to the load value in the time period, and obtain a real-time screenshot load corresponding to the target video sequence, where the real-time screenshot load may represent an average value of the screenshot load in a period of time.
And step 640, updating the load calculation formula based on the real-time screenshot load corresponding to the target video sequence and the expected screenshot load corresponding to the target video sequence to obtain a new load calculation formula.
In this step, after the executing body obtains the real-time screenshot load corresponding to the target video sequence, the real-time screenshot load corresponding to the target video sequence may be compared with the expected screenshot load corresponding to the target video sequence, and the load calculation formula is updated according to the real-time screenshot load corresponding to the target video sequence, so as to adjust the calculation parameters in the load calculation formula, and obtain a new load calculation formula.
The execution body can replace the previous load calculation formula by a new load calculation formula, and the following candidate video sequence can calculate the expected screenshot load by the new load calculation formula.
As an alternative implementation manner, referring to fig. 7, fig. 7 shows a flowchart 700 of one embodiment of updating a load calculation formula, that is, the step 640, based on a real-time screenshot load corresponding to a target video sequence and an expected screenshot load corresponding to the target video sequence, updating the load calculation formula to obtain a new load calculation formula may include the following steps:
And step 710, comparing the real-time screenshot load corresponding to the target video sequence with the expected screenshot load corresponding to the target video sequence, and judging whether the real-time screenshot load corresponding to the target video sequence meets the load condition.
In this step, after the executing body obtains the real-time screenshot load corresponding to the target video sequence, the real-time screenshot load corresponding to the target video sequence may be compared with the expected screenshot load corresponding to the target video sequence, and whether the real-time screenshot load corresponding to the target video sequence meets a load condition is determined, where the load condition may include that the real-time screenshot load is within a preset range, and the preset range may be [ minimum load value, maximum load value ], where the minimum load value is 0.3 of the expected screenshot load, and the maximum load value is 1.3 of the expected screenshot load.
The execution main body can compare the real-time screenshot load corresponding to the target video sequence with a preset range, and judge whether the real-time screenshot load is in the preset range or not so as to determine whether the real-time screenshot load corresponding to the target video sequence meets the load condition or not.
In this step, the executing body determines that the real-time screenshot load corresponding to the target video sequence meets the load condition through judgment, and may perform association marking on the real-time screenshot load corresponding to the target video sequence and the target video sequence, and determine the video information corresponding to the target video sequence. And the execution main body stores the real-time screenshot load corresponding to the target video sequence and the video information of the target video sequence into a sample video sequence set, updates the sample video sequence set and acquires a new sample video sequence set.
And step 710, updating the load calculation formula based on the new sample video sequence set to obtain a new load calculation formula.
In this step, after the executing body obtains the new sample video sequence set, data analysis may be performed on the multiple sample video sequences in the new sample video sequence set, the sample video information of the multiple sample video sequences, and the sample screenshot loads corresponding to the multiple sample video sequences, respectively, and data fitting may be performed on the multiple sample video sequences, the sample video information of the multiple sample video sequences, and the sample screenshot loads corresponding to the multiple sample video sequences by using a regression statistical method in the data analysis method, so as to obtain a load calculation formula corresponding to the new sample video sequence set. And then the execution main body can also utilize analysis of variance to perform error calculation and formula parameter fine adjustment on the obtained load calculation formula and the real-time screenshot load so as to obtain a new load calculation formula.
The execution body can replace the previous load calculation formula by a new load calculation formula, and the following candidate video sequence can calculate the expected screenshot load by the new load calculation formula.
In the implementation mode, the load calculation formula is updated and adjusted in real time through the real-time load feedback mechanism, so that the accuracy of the load calculation formula can be ensured, and the expected screenshot load corresponding to the video sequence can be calculated more accurately.
And if the execution main body determines that the real-time screenshot load corresponding to the target video sequence does not meet the load condition through judgment, discarding the real-time screenshot load, and not serving as new data in a new sample video sequence set.
In the embodiment, the real-time screenshot load is obtained by adding the real-time load feedback mechanism, and the load calculation formula can be updated and adjusted in real time, so that the accuracy of the load calculation formula is ensured, and the predicted screenshot load corresponding to the video sequence can be calculated more accurately.
With continued reference to fig. 6, the method for obtaining a video screenshot may further include the following steps:
In this step, after the executing body obtains a new load calculation formula, the video information of the target video sequence may be brought into the new load calculation formula, and a new predicted screenshot load corresponding to the target video sequence may be calculated.
In step 680, a preset adjustment operation is performed on the target video sequence according to the locally available resources and the new predicted screenshot load corresponding to the target video sequence.
In this step, after the executing body obtains a new expected screenshot load corresponding to the target video sequence, the executing body may compare the new expected screenshot load corresponding to the target video sequence with the locally available resource, and execute a preset adjustment operation on the target video sequence according to the comparison result, where the preset adjustment operation may include adjusting the number of the target video sequences, or adjusting the screenshot speed of the target video sequence.
If the new predicted screenshot load corresponding to the target video sequence is smaller than the locally available resource, the execution body can calculate the new predicted screenshot load of other candidate video sequences in the video sequence set according to the new load calculation formula, and add the new target video sequence according to the new predicted screenshot load, the new predicted screenshot load corresponding to the target video sequence and the locally available resource, so that the number of the target video sequences is increased, and the resource utilization rate is improved.
If the new predicted screenshot load corresponding to the target video sequence is greater than the locally available resource, the execution body may determine that the new predicted screenshot load corresponding to the current target video sequence exceeds the locally available resource, and needs to reduce the screenshot speed of the target video sequence to ensure load balancing.
In this embodiment, by performing a preset adjustment operation on the target video sequence according to the local available resources and the new predicted screenshot load corresponding to the target video sequence, the number of target video sequences or the screenshot speed in the task execution process can be adjusted in real time, so as to ensure the utilization rate of the resources and load balance.
With further reference to fig. 8, as an implementation of the method illustrated in the above figures, the present disclosure provides one embodiment of an apparatus for capturing video shots. This embodiment of the device corresponds to the embodiment of the method shown in fig. 2.
As shown in fig. 8, an apparatus 800 for acquiring a video screenshot of this embodiment may include: the device comprises an acquisition module 810, a calculation module 820, a selection module 830 and an execution module 840.
Wherein the obtaining module 810 is configured to obtain video information of a plurality of candidate video sequences in the video sequence set in response to obtaining the video sequence set for performing the screenshot task;
A calculation module 820 configured to calculate, based on video information of the plurality of candidate video sequences, expected screenshot loads corresponding to the plurality of candidate video sequences;
a selection module 830 configured to select a target video sequence from a set of video sequences based on locally available resources and predicted screenshot loads corresponding to a plurality of candidate video sequences;
the execution module 840 is configured to execute a screenshot task of the target video sequence, so as to obtain a video screenshot corresponding to the target video sequence.
In some alternative implementations of the present implementation, the computing module 820 includes: an acquisition unit configured to acquire a load calculation formula for calculating a screenshot load; and a calculating unit configured to calculate expected screenshot loads corresponding to the plurality of candidate video sequences based on the load calculation formula and video information of the plurality of candidate video sequences.
In some optional implementations of the present implementation, the computing unit is further configured to: acquiring a sample video sequence set, wherein the sample video sequence set comprises a plurality of sample video sequences, sample video information of the plurality of sample video sequences and sample screenshot loads corresponding to the plurality of sample video sequences; and carrying out data analysis on the plurality of sample video sequences, the sample video information of the plurality of sample video sequences and the sample screenshot loads corresponding to the plurality of sample video sequences by using a regression statistical method, and obtaining a load calculation formula related to the sample video information and the sample screenshot loads.
In some optional implementations of the present implementation, the apparatus further includes an update module; the acquisition module 810 is further configured to: responding to the screenshot task of the target video sequence, and acquiring a real-time screenshot load corresponding to the target video sequence; an update module configured to: and updating the load calculation formula based on the real-time screenshot load corresponding to the target video sequence and the expected screenshot load corresponding to the target video sequence to obtain a new load calculation formula.
In some optional implementations of the present implementation, the update module is further configured to: comparing the real-time screenshot load corresponding to the target video sequence with the predicted screenshot load corresponding to the target video sequence, and judging whether the real-time screenshot load corresponding to the target video sequence meets the load condition or not; in response to determining that the real-time screenshot load corresponding to the target video sequence meets a load condition, storing the real-time screenshot load corresponding to the target video sequence and video information of the target video sequence into a sample video sequence set to obtain a new sample video sequence set; and updating the load calculation formula based on the new sample video sequence set to obtain a new load calculation formula.
In some alternative implementations of the present implementation, the computing module 820 is further configured to: calculating a new expected screenshot load corresponding to the target video sequence based on a new load calculation formula and video information of the target video sequence; the execution module 840 is further configured to: and executing preset adjustment operation on the target video sequence according to the locally available resources and the new expected screenshot load corresponding to the target video sequence.
According to the device for acquiring video screenshots provided by the embodiment of the disclosure, the execution main body firstly responds to the acquired video sequence set for executing the screenshot tasks to acquire video information of a plurality of candidate video sequences in the video sequence set, then calculates expected screenshot loads corresponding to the plurality of candidate video sequences based on the video information of the plurality of candidate video sequences, selects a target video sequence from the video sequence set based on local available resources and the expected screenshot loads corresponding to the plurality of candidate video sequences, finally executes the screenshot tasks of the target video sequences to acquire the video screenshots corresponding to the target video sequences, can pre-determine expected screenshot loads corresponding to the screenshot tasks of each video sequence before executing the screenshot tasks, can accurately allocate the quantity of the screenshot tasks in advance, does not need to limit machine resources which can be used by the tasks by setting the thread number of the tasks, can allocate resources for the screenshot tasks based on the accurately calculated expected screenshot loads, can allocate more tasks based on the expected screenshot loads, fully utilizes the local available resources of a server, and improves the load and resource balancing rate.
Those skilled in the art will appreciate that the above-described apparatus also includes some other well-known structures, such as a processor, memory, etc., which are not shown in fig. 8 in order to unnecessarily obscure embodiments of the present disclosure.
Referring now to fig. 9, a schematic diagram of an electronic device 900 suitable for use in implementing embodiments of the present disclosure is shown. The terminal device in the embodiments of the present disclosure may include, but is not limited to, a mobile terminal such as a smart screen, a notebook computer, a PAD (tablet), a PMP (portable multimedia player), an in-vehicle terminal (e.g., in-vehicle navigation terminal), etc., a fixed terminal such as a digital TV, a desktop computer, etc. The terminal device shown in fig. 9 is only one example, and should not impose any limitation on the functions and scope of use of the embodiments of the present disclosure.
As shown in fig. 9, the electronic device 900 may include a processing means (e.g., a central processor, a graphics processor, etc.) 901, which may perform various appropriate actions and processes according to a program stored in a Read Only Memory (ROM) 902 or a program loaded from a storage means 908 into a Random Access Memory (RAM) 903. In the RAM903, various programs and data necessary for the operation of the electronic device 900 are also stored. The processing device 901, the ROM 902, and the RAM903 are connected to each other through a bus 904. An input/output (I/O) interface 905 is also connected to the bus 904.
In general, the following devices may be connected to the I/O interface 905: input devices 906 including, for example, a touch screen, touchpad, keyboard, mouse, camera, microphone, accelerometer, gyroscope, and the like; an output device 907 including, for example, a Liquid Crystal Display (LCD), a speaker, a vibrator, and the like; storage 908 including, for example, magnetic tape, hard disk, etc.; and a communication device 909. The communication means 909 may allow the electronic device 900 to communicate wirelessly or by wire with other devices to exchange data. While fig. 9 shows an electronic device 900 having various means, it is to be understood that not all illustrated means are required to be implemented or provided. More or fewer devices may be implemented or provided instead. Each block shown in fig. 9 may represent one device or a plurality of devices as needed.
In particular, according to embodiments of the present disclosure, the processes described above with reference to flowcharts may be implemented as computer software programs. For example, embodiments of the present disclosure include a computer program product comprising a computer program embodied on a computer readable medium, the computer program comprising program code for performing the method shown in the flowcharts. In such an embodiment, the computer program may be downloaded and installed from a network via the communication device 909, or installed from the storage device 908, or installed from the ROM 902. When executed by the processing device 901, performs the above-described functions defined in the methods of the embodiments of the present disclosure. It should be noted that the computer readable medium of the embodiments of the present disclosure may be a computer readable signal medium or a computer readable storage medium, or any combination of the two. The computer readable storage medium can be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or a combination of any of the foregoing. More specific examples of the computer-readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In an embodiment of the present disclosure, a computer readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. Whereas in embodiments of the present disclosure, the computer-readable signal medium may comprise a data signal propagated in baseband or as part of a carrier wave, with computer-readable program code embodied therein. Such a propagated data signal may take any of a variety of forms, including, but not limited to, electro-magnetic, optical, or any suitable combination of the foregoing. A computer readable signal medium may also be any computer readable medium that is not a computer readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device. Program code embodied on a computer readable medium may be transmitted using any appropriate medium, including but not limited to: electrical wires, fiber optic cables, RF (radio frequency), and the like, or any suitable combination of the foregoing.
Computer program code for carrying out operations for embodiments of the present disclosure may be written in one or more programming languages, including an object oriented programming language such as Java, smalltalk, C ++ and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any kind of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or may be connected to an external computer (for example, through the Internet using an Internet service provider).
The flowcharts and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present application. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
The units involved in the embodiments of the present application may be implemented by software, or may be implemented by hardware. The described units may also be provided in a processor, for example, described as: a processor includes an acquisition module, a calculation module, a selection module, and an execution module, wherein the names of the modules do not constitute a limitation on the module itself in some cases.
As another aspect, the present application also provides a computer readable medium, which may be included in the electronic device described above; or may exist alone without being incorporated into the electronic device. The computer readable medium carries one or more programs which, when executed by the electronic device, cause the electronic device to: in response to acquiring a set of video sequences for performing a screenshot task, acquiring video information of a plurality of candidate video sequences in the set of video sequences; calculating expected screenshot loads corresponding to the candidate video sequences based on the video information of the candidate video sequences; selecting a target video sequence from the video sequence set based on locally available resources and predicted screenshot loads corresponding to the plurality of candidate video sequences; and executing the screenshot task of the target video sequence to obtain the video screenshot corresponding to the target video sequence.
The foregoing description is only of the preferred embodiments of the present disclosure and description of the principles of the technology being employed. It will be appreciated by those skilled in the art that the scope of the invention in the embodiments of the present disclosure is not limited to the specific combination of the above technical features, but encompasses other technical features formed by any combination of the above technical features or their equivalents without departing from the spirit of the invention. Such as the above-described features, are mutually substituted with (but not limited to) the features having similar functions disclosed in the embodiments of the present disclosure.
Claims (14)
1. A method of obtaining a video screenshot, the method comprising:
in response to acquiring a set of video sequences for performing a screenshot task, acquiring video information of a plurality of candidate video sequences in the set of video sequences;
calculating predicted screenshot loads corresponding to the plurality of candidate video sequences based on video information of the plurality of candidate video sequences;
selecting a target video sequence from the video sequence set based on locally available resources and expected screenshot loads corresponding to the plurality of candidate video sequences;
and executing the screenshot task of the target video sequence to obtain the video screenshot corresponding to the target video sequence.
2. The method of claim 1, wherein the calculating, based on video information of the plurality of candidate video sequences, an expected screenshot load for the plurality of candidate video sequences comprises:
acquiring a load calculation formula for calculating a screenshot load;
and calculating the expected screenshot loads corresponding to the candidate video sequences based on the load calculation formula and the video information of the candidate video sequences.
3. The method of claim 2, wherein the obtaining a load calculation formula for calculating a screenshot load comprises:
acquiring a sample video sequence set, wherein the sample video sequence set comprises a plurality of sample video sequences, sample video information of the plurality of sample video sequences and sample screenshot loads corresponding to the plurality of sample video sequences;
and carrying out data analysis on the plurality of sample video sequences, the sample video information of the plurality of sample video sequences and the sample screenshot loads corresponding to the plurality of sample video sequences by using a regression statistical method, and obtaining a load calculation formula associated with the sample video information and the sample screenshot loads.
4. A method according to any one of claims 1-3, the method further comprising:
Responding to the execution of the screenshot task of the target video sequence, and acquiring a real-time screenshot load corresponding to the target video sequence;
and updating the load calculation formula based on the real-time screenshot load corresponding to the target video sequence and the expected screenshot load corresponding to the target video sequence to obtain a new load calculation formula.
5. The method of claim 4, wherein updating the load calculation formula based on the real-time screenshot load corresponding to the target video sequence and the predicted screenshot load corresponding to the target video sequence to obtain a new load calculation formula comprises:
comparing the real-time screenshot load corresponding to the target video sequence with the expected screenshot load corresponding to the target video sequence, and judging whether the real-time screenshot load corresponding to the target video sequence meets the load condition or not;
in response to determining that the real-time screenshot load corresponding to the target video sequence meets a load condition, storing the real-time screenshot load corresponding to the target video sequence and video information of the target video sequence into a sample video sequence set to obtain a new sample video sequence set;
And updating the load calculation formula based on the new sample video sequence set to obtain a new load calculation formula.
6. The method of claim 4, the method further comprising:
calculating a new expected screenshot load corresponding to the target video sequence based on the new load calculation formula and the video information of the target video sequence;
and executing preset adjustment operation on the target video sequence according to the locally available resources and the new expected screenshot load corresponding to the target video sequence.
7. An apparatus for obtaining a video screenshot, the apparatus comprising:
an acquisition module configured to acquire video information of a plurality of candidate video sequences in a set of video sequences in response to acquiring the set of video sequences for performing a screenshot task;
a calculation module configured to calculate predicted screenshot loads corresponding to the plurality of candidate video sequences based on video information of the plurality of candidate video sequences;
a selection module configured to select a target video sequence from the set of video sequences based on locally available resources and predicted screenshot loads corresponding to the plurality of candidate video sequences;
And the execution module is configured to execute the screenshot task of the target video sequence to obtain the video screenshot corresponding to the target video sequence.
8. The apparatus of claim 7, wherein the computing module comprises:
an acquisition unit configured to acquire a load calculation formula for calculating a screenshot load;
and a calculating unit configured to calculate predicted screenshot loads corresponding to the plurality of candidate video sequences based on the load calculation formula and video information of the plurality of candidate video sequences.
9. The apparatus of claim 8, wherein the computing unit is further configured to:
acquiring a sample video sequence set, wherein the sample video sequence set comprises a plurality of sample video sequences, sample video information of the plurality of sample video sequences and sample screenshot loads corresponding to the plurality of sample video sequences;
and carrying out data analysis on the plurality of sample video sequences, the sample video information of the plurality of sample video sequences and the sample screenshot loads corresponding to the plurality of sample video sequences by using a regression statistical method, and obtaining a load calculation formula associated with the sample video information and the sample screenshot loads.
10. The apparatus of any of claims 7-9, wherein the apparatus further comprises an update module;
the acquisition module is further configured to: responding to the execution of the screenshot task of the target video sequence, and acquiring a real-time screenshot load corresponding to the target video sequence;
the update module is configured to: and updating the load calculation formula based on the real-time screenshot load corresponding to the target video sequence and the expected screenshot load corresponding to the target video sequence to obtain a new load calculation formula.
11. The apparatus of claim 10, wherein the update module is further configured to:
comparing the real-time screenshot load corresponding to the target video sequence with the expected screenshot load corresponding to the target video sequence, and judging whether the real-time screenshot load corresponding to the target video sequence meets the load condition or not;
in response to determining that the real-time screenshot load corresponding to the target video sequence meets a load condition, storing the real-time screenshot load corresponding to the target video sequence and video information of the target video sequence into a sample video sequence set to obtain a new sample video sequence set;
And updating the load calculation formula based on the new sample video sequence set to obtain a new load calculation formula.
12. The apparatus of claim 10, wherein the computing module is further configured to: calculating a new expected screenshot load corresponding to the target video sequence based on the new load calculation formula and the video information of the target video sequence;
the execution module is further configured to: and executing preset adjustment operation on the target video sequence according to the locally available resources and the new expected screenshot load corresponding to the target video sequence.
13. An electronic device, comprising:
one or more processors;
storage means for storing one or more programs,
the one or more programs, when executed by the one or more processors, cause the one or more processors to implement the method of any of claims 1-6.
14. A computer readable medium on which a computer program is stored, characterized in that the computer program, when being executed by a processor, implements the method according to any one of claims 1-6.
Priority Applications (2)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202211249420.4A CN116095414A (en) | 2022-10-12 | 2022-10-12 | Method and device for acquiring video screenshot |
| PCT/CN2023/089184 WO2024077916A1 (en) | 2022-10-12 | 2023-04-19 | Video screenshot acquiring method and apparatus |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202211249420.4A CN116095414A (en) | 2022-10-12 | 2022-10-12 | Method and device for acquiring video screenshot |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN116095414A true CN116095414A (en) | 2023-05-09 |
Family
ID=86198021
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202211249420.4A Pending CN116095414A (en) | 2022-10-12 | 2022-10-12 | Method and device for acquiring video screenshot |
Country Status (2)
| Country | Link |
|---|---|
| CN (1) | CN116095414A (en) |
| WO (1) | WO2024077916A1 (en) |
Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20170238000A1 (en) * | 2016-02-16 | 2017-08-17 | Gachon University Of Industry-Academic Cooperation Foundation | Parallel video processing apparatus using multicore system and method thereof |
| CN108733821A (en) * | 2018-05-22 | 2018-11-02 | 武汉微创光电股份有限公司 | A kind of distribution of monitor video sectional drawing and methods of exhibiting and system |
| CN109325145A (en) * | 2018-09-19 | 2019-02-12 | 上海哔哩哔哩科技有限公司 | Acquisition methods, terminal and the computer readable storage medium of video thumbnails |
| CN111757046A (en) * | 2019-03-28 | 2020-10-09 | 菜鸟智能物流控股有限公司 | Data processing method, device, equipment and machine readable medium |
| CN114244848A (en) * | 2021-12-15 | 2022-03-25 | 中国电信股份有限公司 | Candidate streaming media determining and load balancing method, device, server and medium |
-
2022
- 2022-10-12 CN CN202211249420.4A patent/CN116095414A/en active Pending
-
2023
- 2023-04-19 WO PCT/CN2023/089184 patent/WO2024077916A1/en unknown
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20170238000A1 (en) * | 2016-02-16 | 2017-08-17 | Gachon University Of Industry-Academic Cooperation Foundation | Parallel video processing apparatus using multicore system and method thereof |
| CN108733821A (en) * | 2018-05-22 | 2018-11-02 | 武汉微创光电股份有限公司 | A kind of distribution of monitor video sectional drawing and methods of exhibiting and system |
| CN109325145A (en) * | 2018-09-19 | 2019-02-12 | 上海哔哩哔哩科技有限公司 | Acquisition methods, terminal and the computer readable storage medium of video thumbnails |
| CN111757046A (en) * | 2019-03-28 | 2020-10-09 | 菜鸟智能物流控股有限公司 | Data processing method, device, equipment and machine readable medium |
| CN114244848A (en) * | 2021-12-15 | 2022-03-25 | 中国电信股份有限公司 | Candidate streaming media determining and load balancing method, device, server and medium |
Also Published As
| Publication number | Publication date |
|---|---|
| WO2024077916A1 (en) | 2024-04-18 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| US9106521B2 (en) | Method and system for visualizing an adaptive screen according to a terminal | |
| CN111475298A (en) | Task processing method, device, equipment and storage medium | |
| CN114401447B (en) | Video clamping prediction method, device, equipment and medium | |
| CN112135169B (en) | Media content loading method, device, equipment and medium | |
| CN110213614B (en) | Method and device for extracting key frame from video file | |
| CN112272226B (en) | Picture loading method and device and readable storage medium | |
| CN110795022A (en) | Terminal testing method, system and storage medium | |
| CN112256223B (en) | Method and device for adjusting display fluency and electronic equipment | |
| CN113163002B (en) | Server switching method and device and storage medium | |
| CN113254105B (en) | Resource processing method and device, storage medium and electronic equipment | |
| CN110619096B (en) | Method and apparatus for synchronizing data | |
| CN112995776B (en) | Method, device, equipment and storage medium for determining screen capture frame rate of shared screen content | |
| CN111246228B (en) | Method, device, medium and electronic equipment for updating gift resources of live broadcast room | |
| CN112954354B (en) | Video transcoding method, device, equipment and medium | |
| CN110636367A (en) | Video loading method and device, terminal equipment and medium | |
| CN110768861B (en) | Method, device, medium and electronic equipment for obtaining overtime threshold | |
| CN110418134B (en) | Video coding method and device based on video quality and electronic equipment | |
| CN111209432A (en) | Information acquisition method and device, electronic equipment and computer readable medium | |
| CN108769754B (en) | Video playback method and system based on video playback terminal | |
| CN110809166B (en) | Video data processing method and device and electronic equipment | |
| CN117241092A (en) | Video processing method and device, storage medium and electronic equipment | |
| CN115834918B (en) | Video live broadcast method and device, electronic equipment and readable storage medium | |
| CN116095414A (en) | Method and device for acquiring video screenshot | |
| CN116319932A (en) | Training method, device, equipment and storage medium of content push model | |
| CN113837814B (en) | Method and device for predicting quantity of released resources, readable medium and electronic equipment |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination |