EP2039172A2 - Calcul de complexité d'image dans des systèmes de diffusion vidéo à base de paquets - Google Patents

Calcul de complexité d'image dans des systèmes de diffusion vidéo à base de paquets

Info

Publication number
EP2039172A2
EP2039172A2 EP06787390A EP06787390A EP2039172A2 EP 2039172 A2 EP2039172 A2 EP 2039172A2 EP 06787390 A EP06787390 A EP 06787390A EP 06787390 A EP06787390 A EP 06787390A EP 2039172 A2 EP2039172 A2 EP 2039172A2
Authority
EP
European Patent Office
Prior art keywords
video
discrete sections
video stream
complexity
bandwidth
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.)
Ceased
Application number
EP06787390A
Other languages
German (de)
English (en)
Other versions
EP2039172A4 (fr
Inventor
Praveen A. Mohandas
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Microsemi Frequency and Time Corp
Original Assignee
Symmetricom Inc
Priority date (The priority date 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 date listed.)
Filing date
Publication date
Priority claimed from US11/456,505 external-priority patent/US8107540B2/en
Application filed by Symmetricom Inc filed Critical Symmetricom Inc
Publication of EP2039172A2 publication Critical patent/EP2039172A2/fr
Publication of EP2039172A4 publication Critical patent/EP2039172A4/fr
Ceased legal-status Critical Current

Links

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
    • H04N21/43—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
    • H04N21/434—Disassembling of a multiplex stream, e.g. demultiplexing audio and video streams, extraction of additional data from a video stream; Remultiplexing of multiplex streams; Extraction or processing of SI; Disassembling of packetised elementary stream
    • H04N21/4347—Demultiplexing of several video streams
    • 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
    • H04N21/23—Processing of content or additional data; Elementary server operations; Server middleware
    • H04N21/238—Interfacing the downstream path of the transmission network, e.g. adapting the transmission rate of a video stream to network bandwidth; Processing of multiplex streams
    • 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
    • H04N21/23—Processing of content or additional data; Elementary server operations; Server middleware
    • H04N21/236—Assembling of a multiplex stream, e.g. transport stream, by combining a video stream with other content or additional data, e.g. inserting a URL [Uniform Resource Locator] into a video stream, multiplexing software data into a video stream; Remultiplexing of multiplex streams; Insertion of stuffing bits into the multiplex stream, e.g. to obtain a constant bit-rate; Assembling of a packetised elementary stream
    • H04N21/2365—Multiplexing of several video streams
    • 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
    • H04N21/23—Processing of content or additional data; Elementary server operations; Server middleware
    • H04N21/236—Assembling of a multiplex stream, e.g. transport stream, by combining a video stream with other content or additional data, e.g. inserting a URL [Uniform Resource Locator] into a video stream, multiplexing software data into a video stream; Remultiplexing of multiplex streams; Insertion of stuffing bits into the multiplex stream, e.g. to obtain a constant bit-rate; Assembling of a packetised elementary stream
    • H04N21/2365—Multiplexing of several video streams
    • H04N21/23655—Statistical multiplexing, e.g. by controlling the encoder to alter its bitrate to optimize the bandwidth utilization
    • 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
    • H04N21/23—Processing of content or additional data; Elementary server operations; Server middleware
    • H04N21/24—Monitoring of processes or resources, e.g. monitoring of server load, available bandwidth, upstream requests
    • H04N21/2402—Monitoring of the downstream path of the transmission network, e.g. bandwidth available
    • 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
    • H04N21/266—Channel or content management, e.g. generation and management of keys and entitlement messages in a conditional access system, merging a VOD unicast channel into a multicast channel
    • H04N21/2662—Controlling the complexity of the video stream, e.g. by scaling the resolution or bitrate of the video stream based on the client capabilities

Definitions

  • the present invention relates generally to broadcast systems. More particularly, the present invention pertains to methods of estimating the complexity of a series of images in compressed video programs that use MPEG compatible encoding.
  • [Para 2] In typical broadcast systems, such as in IPTV (Internet Protocol Television) and direct broadcast satellite (DBS) applications, multiple video programs are encoded in parallel, and the digitally compressed bitstreams are multiplexed onto a single, constant or variable bit rate channel. The available channel bandwidth could be distributed unevenly among programs, in proportion to the information content/complexity of each of the video sources.
  • the monitoring system that computes video quality by measuring impairments could take into account the image complexity factor of the video stream to calculate the different effects of impairments on lesser or more complex images.
  • VBR variable bit rate
  • FIG. 1 shows components that are involved in delivering video content in a typical IPTV environment.
  • Video source that originates as analog signal is encoded using an encoder and packetized and sent using an IP network. It could be sent as manage subscribers and traffic flows.
  • the content is stored in content servers and delivered on demand upon user request. At various points in the network, measurements can be performed for impairments by service assurance managements systems.
  • MPEG coding standards define three picture types (I, B and P) and encodes pictures with a fixed arrangement. Picture type changes could occur due to scene transitions. In the event of an abrupt transition, the first frame of the new scene is intra- coded (l-frame) in order to avoid severe coding errors. During a gradual scene transition, the distance between two reference frames (I or P) can be changed to improve the picture quality. During most of these gradual transitions, temporal correlation tends to be reduced. This situation demands more frequent placement of predicted reference frames (P-frames) to uphold the required picture quality. When the video sequence contains rapid motions, this may also require frequent P-frames in order to improve picture quality. This increases the bit rate.
  • P-frames predicted reference frames
  • VCL Video Coding Layer
  • the process for broadcasting multiple video streams on a single channel begins with analyzing complexity indication changes and bit rate changes in a video coding layer of each of the multiple video streams.
  • a statistical model is created to dynamically compute the image complexity of each of the multiple video streams.
  • the effect of the image complexity of each of the multiple video streams on the broadcast is then determined. Available channel bandwidth is distributed among the multiple video streams based upon the determined effect of the image complexity of each of the multiple video streams.
  • the process further involves estimating video quality in certain loss states.
  • Analyzing the complexity indication changes involves analyzing changes in parameters of discrete sections of the video streams.
  • the discrete sections of the video streams include slice, macroblocks, quantization, inter-coded reference blocks, intra-coded reference blocks, and non-reference macroblock/slice/picture types.
  • Creating the statistical model involves creating a first statistical model of video coding layer complexity indication changes for discrete sections of each video stream. Further, a second statistical model of video coding layer bit rate changes or bandwidth variation is created for the same discrete sections of each video stream. The first and second statistical models from the discrete sections of each video stream are then combined.
  • Image complexity of the discrete sections of each video stream is calculated based upon the combined first and second statistical models.
  • types for picture/slice/macroblock types are counted by determining quantization changes in each video stream.
  • Bandwidth variation is counted by determining the bandwidth of the video coding layer data in each video stream. The counting is accomplished by incrementing a first counter for each quantization change, a second counter for each macroblock, a third counter for each slice, and a fourth counter for each low, average and high bandwidth state transition.
  • a probability for complexity of the video coding layer complexity for discrete sections of each video stream is computed using the first, second, third and fourth counters. Further, a probability for low, average and high bandwidth states for the discrete sections for each video stream is computed using the first, second, third and fourth counters.
  • a first transition probability matrix is constructed for video coding layer complexity transition of the discrete sections of each video stream and a second transition probability matrix is constructed for bandwidth state transition of the discrete sections of each video stream.
  • An image complexity value of the discrete sections of each video stream is computed using limiting state probabilities obtained from each transition probability matrix.
  • the method can be used by collectors to get image complexity value from distributed remote probes; to facilitate computation of impairments in packetized video stream using image complexity as a variable to get more accuracy towards perceived video quality; to provide image complexity at regular intervals for packetized video applications; to provide an estimation on video complexity as perceived by human visual system; to provide Image complexity measurements for typical industry wide video quality assessment models, including and not limited to Peak Signal to Noise Ratio (PSNR), MPQM, MQUANT and Root Mean Square Error (RMSE); to provide offline and real time image complexity , VOD servers (video on demand), broadcast servers and video quality measurement equipments; to provide a statistical model for bandwidth variation that contributes to image complexity; to provide a statistical model for video coding layer complexity that contributes to scene transitions; and to determine the statistical distribution of series of images in a low complexity state and a high complexity state.
  • PSNR Peak Signal to Noise Ratio
  • MPQM MPQM
  • MQUANT and Root Mean Square Error RMSE
  • VOD servers video on demand
  • FIGURE 1 shows an example of an IPTV (IP television) distribution network with potential points where measurements for image complexity could be done;
  • IPTV IP television
  • FIGURE 2 shows a typical protocol stack where MPEG frames are encapsulated in
  • IP Internet Protocol
  • FIGURE 3 shows a statistical model for computing Image Complexity with the final curve fit equation
  • FIGURE 4 shows a Markov transition process for a Bandwidth model
  • FIGURE 5 shows a Markov transition process for a Video Coding Layer
  • FIGURE 6 shows the counters and transition matrix relationship for the
  • FIGURE 8 shows the transition probability matrix for a Bandwidth variation model
  • FIGURE 9 shows the transition probability matrix for Video Coding Layer complexity model
  • FIGURE 10 shows the probability values and curve fit equation relationship that computes image complexity
  • FIGURE 1 1 shows the flowchart for the bandwidth and Video Coding Layer model computation
  • FIGS. 2-10 A preferred embodiment of the present invention is illustrated in FIGS. 2-10.
  • An embodiment of the present invention can be utilized in an IPTV delivery system such as that illustrated in FIG. 1 .
  • the present invention relates to a method of estimating image complexity in a series of images in a video stream supporting MPEG type picture encoding.
  • the method includes creating, during a flow of encoded video stream, a statistical model representing the VCL parameters as quantization, macroblock/slice counts, macroblock sizes 16x16, 1 6x8, 8x8, 4x4, 8x16, picture type variation as inter, intra, I/B/P frame/macroblock types variation that determines the probability of causing scene transitions.
  • a statistical model representing bandwidth variation that determines the probability of high and low bandwidth states is also created. Image complexity is then determined from the two statistical models created from the same flow estimate perceived video complexity.
  • the method also includes: determining the quantization changes to count the high quantization transitions, slice/macroblock counts for the monitoring interval, Inter/lntra prediction types for picture/slice/macroblock types (I, B, P) and determining the bandwidth of VCL data to count the bandwidth variation; incrementing a counter for quantization changes, incrementing counters for macroblock and slice types and sizes, and incrementing a counter for bandwidth low, average and high state transitions; computing probability from the counters for state transitions for video coding layer complexity, and computing probability from the counters for state transitions for low, average and high bandwidth states; and computing a transition probability matrix for video coding layer complexity transition and computing a transition probability matrix for bandwidth state transition.
  • FIG. 1 shows a typical IPTV distribution network 21 that includes Video content acquisition 1 2, IPTV management system 14, IPTV content distribution 16 and IPTV consumer 18.
  • Video Source 20 is usually acquired in analog form and encoded in MPEG 1 /2/4 format by a video encoder 22 and sent to a Video on Demand (VOD) server 24 or a Broadcast server 26.
  • VOD Video on Demand
  • the VOD server 24 encapsulates the content into a program stream for transport to a network core 28.
  • the network core 28 is a relatively higher bandwidth pipe.
  • An IPTV network 21 also consists of a variety of management, provisioning and service assurance elements. Typically it includes the Operation Support System (OSS) 30, Subscriber management system 32 and application servers 34 to create new value added services.
  • OSS Operation Support System
  • the content could be stored in a VOD server 36 or a broadcast server 38 that is accessible by the consumer. It is typically located at an edge 40 of the network 21 . A consumer has , is typically connected to a setop box 46 that decodes the video stream to component output.
  • a protocol stack for a packetized video stream is illustrated in FIG. 2.
  • Media dependent attachment 48 could be Ethernet, Sonet, DS3, cable, or DSL interface.
  • a PHY 50 does the media dependent packet processing.
  • An IP (Internet Protocol) 52 is the network layer part that provides mainly addressing for packet routing in the IPTV network 21 .
  • a UDP/RTP 54 is the transport layer that provides application level addressing for ports.
  • the video stream can be encapsulated in the UDP/RTP or just UDP layer 54.
  • the encoded video can be compressed in MPEG 1 /2/4 and sent as a transport stream or in RTP encapsulation for video 56.
  • a video coding layer packet input 60 is decoded and necessary parameters are extracted to get the values for measurement 62 for the image complexity model, as described below.
  • FiG. 3 provides the high level logic for the statistical models in an embodiment of the present invention.
  • MPEG VCL input 64 is provided to both a VCL complexity (l-frame) model 66 and a bandwidth model 68 to compute the counters needed for the statistical models.
  • a curve fit equation 70 takes the model output parameters and computes image complexity 72.
  • FIG. 4 illustrates discrete Markov process state transitions for the bandwidth model 68.
  • the bandwidth variations in video sequence are modeled into a three state Markov process to determine the probability of low and high bandwidth state transitions.
  • State one (Sl ) 74, State two (S2) 76 and State three (S3) 78 respectively represent states of the model 68 in low, constant and high bandwidth states. . . « complexity quantization model 66.
  • the quantization transitions retrieved from the macroblock layer is modeled into a two state Markov process.
  • Kl 80 and K2 82 show states of the VCL layer complexity model 66 - quantization high and quantization low states.
  • a VCL bandwidth monitor 84 monitors the bandwidth variations in the VCL stream and updates counters cXY 86, where X represents the initial state and Y represents the resulting state.
  • the initial and resulting states may be low, constant or high bandwidth states designated as 1 , 2 or 3, respectively.
  • Cl 1 represents the state transition event from a low bandwidth state 74 to a low bandwidth state 74
  • C23 represents the state transition event from a constant bandwidth state 76 to a high bandwidth state 78.
  • State transition probabilities 90 are computed to get a transition matrix 88.
  • the State transition probabilities 90 are represented by pXY where X represents the initial state and Y represents the resulting state.
  • the initial and resulting states may be low, constant or high bandwidth states designated as 1 , 2 or 3, respectively.
  • pi 2 is the transition probability to go from the low bandwidth state (Sl ) 74 to the constant bandwidth state (S2) 76.
  • the transition matrix 88 is formed.
  • limiting state probabilities are computed without the initial conditions to get BPl 01 92 and BPl 03 94. These values represent the probability to stay in the low bandwidth state and the high bandwidth state, respectively.
  • Counters 98 used to compute the transition probabilities for the VCL layer complexity quantization model are seen in FIG. 7.
  • a VCL slice and macroblock monitor 96 monitors the quantization parameter in the macroblock and updates counters dXY where X represents the initial state and Y represents the resulting state. The initial and resulting , , respectively.
  • dl 2 represents the state transition event counts from a quantization high received state to a quantization low received state.
  • State transition probabilities are computed to get a transition matrix 100 for the VCL layer quantization model 66. From the transition matrix 100, the probability of a high quantization occurrence in a picture sequence is computed and set in variable IPl 00 1 02.
  • the transition probability matrix 88 for the bandwidth model 68 is illustrated in FIG. 8.
  • States Sl 74, S2 76 and S3 78 represent low, average and high bandwidth states, as outlined above, and each cell in the matrix 88 represents the probability of state transition from one state to another.
  • FIG. 9 shows a transition probability matrix 100 for the VCL layer quantization model 66.
  • States Kl 104 and K2 106 represent high quantization and low quantization occurrence states, and each cell in the matrix 100 represents the probability of state transition from one state to another.
  • FIG. 10 illustrates the VCL layer complexity model 66 and bandwidth model 68 out parameters BPl 01 92, BPl 03 94 and IPl 00 1 02 used in the curve fit equation 73 of FIG. 3 to get image complexity (F) 72 which ranges in value from 2 to 3.
  • FIG. 1 1 illustrates a flow chart for the main functional blocks of the inventive process.
  • a bandwidth model initialization 108 is the first step that needs to be performed to run the bandwidth 68 and VCL layer complexity 66 models. Variables to compute an average bandwidth are initialized 1 10.
  • a VCL input is read from the NAL (Network Abstraction Layer)/ transport stream 1 1 2.
  • Average bandwidth for the VCL packets is computed 1 14 and set 1 1 6.
  • a bandwidth model 68 and a VCL layer complexity model 66 are run in parallel 1 1 8.
  • the bandwidth model 68 is initialized for transition counters 1 20.
  • the VCL packet size is read from the NAL/transport layer stream .
  • transition probability matrix is updated 1 28.
  • the next step is to compute the high and state limiting state probabilities 1 30 using equations (1 ) and (2), as detailed below.
  • the variables BPl Ol and BPl 03 are set 1 32. For every macroblock, the VCL complexity model
  • the counters are initialized 1 36 and macroblock and slice quantization parameters are read from the NAL/transport stream 1 38 by decoding slice data from the VCL
  • the VCL complexity quantization transition probability matrix is computed 140 and limiting state probabilities are computed 142.
  • the IPl OO variable is then set 144.
  • the final curve fit equation is computed 146 using variables BPl 01 , BPl 03 and IPl OO.
  • a bandwidth model 68 is constructed using the Markov model in FIG. 4.
  • the bandwidth model 68 is initialized after the MPEG video stream creation. At this stage, the bandwidth model 68 determines average bandwidth of the video stream for each discrete section, i.e., at every sampling instance.
  • the procedure to determine average bandwidth is as follows: o Initialize counters Al 00, Al 01 , Al 02, Al 03, Al 04 to zero; o From the MPEG layer read VCL packet size for every NAL / transport layer packet received and set Al 00 for cumulative size received; o Increment Al 03 for every INTRA macroblock/picture type; o Increment Al 04 for every slice type; o Set Al 01 to first VCL received time in milliseconds; o Set Al 02 for every VCL received time in milliseconds; and o At each sampling instance, compute average bandwidth. n v * .
  • Al OO Al OO + VCL_size_rcvd from MPEG layer
  • the model will be in the bandwidth low state (Sl ) if the current video stream bandwidth is lower than Cl 00 - 10 kbps; for bandwidths higher than
  • the model will be in the bandwidth high state (S3). If the bandwidth is within the average bandwidth value, the model is in the bandwidth constant state (S2).
  • Average bandwidth (Cl 00) is determined continuously for the VCL packets, the bandwidth variation can be modeled using the Discrete transition Markov Process illustrated in FIG. 4.
  • the three states' (Sl , S2 and S3) transitions are calculated by monitoring the video stream bandwidth variation.
  • the transition matrix 88 (FIG. 8) is obtained, where each cell represents the probability of a state transition from a particular state to the next state.
  • the transition matrix 88 is constructed from the MPEG video stream bandwidth variation statistics.
  • the transition matrix 88 is obtained by computing the probability to transition from a particular state to any other possible state, as illustrated in FIG. 8. For instance, the probability of staying in state Sl is represented by pi 1 .
  • a transition matrix 88 is computed from the above.
  • the transition probabilities are calculated from the relative frequencies of state transition.
  • pi 1 cl 1 / (el l + cl 2 + cl 3)
  • pi 2 cl 2 / (el l + cl 2 + cl 3)
  • pi 3 cl 3 / (el l + cl 2 + cl 3)
  • p21 c21 / (c21 + c22 + c23)
  • p22 c22 / (c21 + c22 + c23)
  • p23 c23 / (c21 + c22 + c23)
  • p31 c31 / (c31 + c32 + c33)
  • p32 c32 / (c31 + c32 + c33)
  • p33 c33 / (c31 + c32 + c33)
  • VCL complexity probability follows a process similar to the one described above, but the Markov states are limited to two states.
  • FIG. 10 shows the state transition process of the VCL complexity model. The states represent:
  • Each cell represents the state transition, e.g., pi 2 represents the probability of having a quantization low (K2) in quantization high received state (Kl ).
  • K2 quantization low
  • Kl quantization high received state

Abstract

L'invention concerne un procédé de détermination de complexité d'image en temps réel dans des applications IPTV, de diffusion et de transmission en continu de vidéos, au moyen d'un modèle statistique, représentant la variation de largeur de bande des canaux et la complexité d'image, qui prend en compte les changements de contenu des scènes. La largeur de bande disponible des canaux est distribuée de manière irrégulière parmi de multiples séquences vidéo en proportion de la variation de largeur de bande et de la complexité d'image de chaque séquence vidéo diffusée. La distribution de la largeur de bande disponible des canaux est déterminée en se basant sur le facteur de complexité d'image de chaque séquence vidéo, comme déterminé à partir de matrices de probabilité prenant en compte les variations de largeur de bande et la complexité d'image.
EP06787390A 2006-07-10 2006-07-12 Calcul de complexité d'image dans des systèmes de diffusion vidéo à base de paquets Ceased EP2039172A4 (fr)

Applications Claiming Priority (2)

Application Number Priority Date Filing Date Title
US11/456,505 US8107540B2 (en) 2005-07-11 2006-07-10 Image complexity computation in packet based video broadcast systems
PCT/US2006/027477 WO2008127217A2 (fr) 2006-07-10 2006-07-12 Calcul de complexité d'image dans des systèmes de diffusion vidéo à base de paquets

Publications (2)

Publication Number Publication Date
EP2039172A2 true EP2039172A2 (fr) 2009-03-25
EP2039172A4 EP2039172A4 (fr) 2011-04-13

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EP (1) EP2039172A4 (fr)
JP (1) JP2009543513A (fr)
KR (1) KR20090045882A (fr)
CN (1) CN101502112A (fr)
BR (1) BRPI0614591A2 (fr)
CA (1) CA2622548A1 (fr)
MX (1) MX2008014372A (fr)
WO (1) WO2008127217A2 (fr)

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Publication number Priority date Publication date Assignee Title
EP2383999A1 (fr) * 2010-04-29 2011-11-02 Irdeto B.V. Contrôle d'une diffusion adaptative de contenu numérique
US8997160B2 (en) * 2010-12-06 2015-03-31 Netflix, Inc. Variable bit video streams for adaptive streaming
US9125073B2 (en) * 2012-08-03 2015-09-01 Intel Corporation Quality-aware adaptive streaming over hypertext transfer protocol using quality attributes in manifest file
EP3742739B1 (fr) * 2019-05-22 2021-04-14 Axis AB Procédé et dispositifs de codage et de diffusion en continu d'une séquence vidéo sur une pluralité de connexions réseau
CN113360094B (zh) * 2021-06-04 2022-11-01 重庆紫光华山智安科技有限公司 数据预测方法和装置、电子设备及存储介质
WO2023233631A1 (fr) * 2022-06-02 2023-12-07 日本電信電話株式会社 Dispositif d'estimation de qualité vidéo, procédé d'estimation de qualité vidéo et programme

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US5018215A (en) * 1990-03-23 1991-05-21 Honeywell Inc. Knowledge and model based adaptive signal processor
US6310915B1 (en) * 1998-11-20 2001-10-30 Harmonic Inc. Video transcoder with bitstream look ahead for rate control and statistical multiplexing

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No further relevant documents disclosed *
See also references of WO2008127217A2 *

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EP2039172A4 (fr) 2011-04-13
JP2009543513A (ja) 2009-12-03
WO2008127217A2 (fr) 2008-10-23
KR20090045882A (ko) 2009-05-08
BRPI0614591A2 (pt) 2012-01-24
MX2008014372A (es) 2008-11-24
CA2622548A1 (fr) 2008-01-10
CN101502112A (zh) 2009-08-05
WO2008127217A3 (fr) 2009-04-16

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