Digital Video Compression Digital Video Compression Digital Video - - PowerPoint PPT Presentation
Digital Video Compression Digital Video Compression Digital Video Compression and H.261 Recommendation and H.261 Recommendation and H.261 Recommendation Fernando Pereira Fernando Pereira Fernando Pereira Klagenfurt, Austria, October 2008
Audiovisual Compression: from Basics to Systems, Fernando Pereira
Fernando Pereira Klagenfurt, Austria, October 2008
Fernando Pereira Fernando Pereira Klagenfurt, Austria, October 2008
Audiovisual Compression: from Basics to Systems, Fernando Pereira
Video versus Images Video versus Images Video versus Images
Still Image Services – No strong temporal requirements; no real- time notion.
Video Services (moving images) – There is a need to strictly follow strong delay requirements to provide a good illusion of motion; essential to provide real-time performance. For each image and video service, it is possible to associate a quality target (quality of service); the first impact of this target is the selection of the right spatial and temporal resolutions to use.
Audiovisual Compression: from Basics to Systems, Fernando Pereira
Why Does Video Information Have to be Compressed ? Why Does Video Information Have to be Compressed Why Does Video Information Have to be Compressed ? ?
A video sequence is created and consumed as a set of images, happening at a certain temporal rate (F), each
× × ×N luminance and chrominance samples and a certain number of bits per sample (L) This means the total number of (PCM) bits
necessary to digitally represent a video sequence is HUGE !!!
Audiovisual Compression: from Basics to Systems, Fernando Pereira
Videotelephony: Just an Example Videotelephony: Just an Example Videotelephony: Just an Example
× × ×360 luminance samples and 144 × × × × 188 samples for each chrominance, with 8 bit/sample [(360 ×
× × × 288) + 2 × × × × (180 × × × × 144)] × × × × 8 × × × × 10 = 12.44 Mbit/s
=> Compression Factor: 12.44 Mbit/s/64 kbit/s => Compression Factor: 12.44 Mbit/s/64 kbit/s ≈ ≈ ≈ ≈ ≈ ≈ ≈ ≈ 194 194 The usage or not of compression/source coding implies the The usage or not of compression/source coding implies the possibility or not to deploy services and, thus, the possibility or not to deploy services and, thus, the existence or not of certain industries, e.g. DVD. existence or not of certain industries, e.g. DVD.
Audiovisual Compression: from Basics to Systems, Fernando Pereira
Digital Video: Why is it So Difficult ? Digital Video: Why is it So Difficult ? Digital Video: Why is it So Difficult ?
Serviço Resolução espacial –
Resolução espacial –
Resolução temporal Factor de forma Débito binário (PCM) TV alta definição 1152 × 1920 576 × 960 50 img/s 16/9 1.3 Gbit/s TV (qualidade difusão, DVD) 576 × 720 576 × 360 25 img/s entrelaçadas 4/3 166 Mbit/s TV (gravação CD) 288 × 360 144 × 180 25 img/s progressivas 4/3 31 Mbit/s Videotelefonia e Videoconfer. 288 × 360 144 × 180 10 img/s progressivas 4/3 12.4 Mbit/s Videotelefonia móvel 144 × 180 72 × 90 5 img/s progressivas 4/3 1.6 Mbit/s
Audiovisual Compression: from Basics to Systems, Fernando Pereira
Video Coding/Compression: a Definition Video Coding/Compression: Video Coding/Compression: a a Definition Definition
Efficient representation (this means with a smaller than the PCM number of bits) of a periodic sequence of (correlated) images, satisfying the relevant requirements, e.g. Minimum acceptable quality, error robustness, random access. And the service requirements change with the services/applications and the corresponding funcionalities ...
Audiovisual Compression: from Basics to Systems, Fernando Pereira
How Big Have to be the Compression Factors ? How Big How Big Have to be Have to be the the Compression Compression Factors ? Factors ?
Serviço Resolução espacial Resolução temporal Débito binário (PCM) Débito binário codificado Factor de compressão TV alta definição 1152 × 1920 576 × 960 50 img/s 1.3 Gbit/s 34 Mbit/s 38.2 TV alta definição 1152 × 1920 576 × 960 50 img/s 1.3 Gbit/s 17 Mbit/s 76.5 TV (qualidade difusão, DVD) 576 × 720 576 × 360 25 img/s entrelaçadas 166 Mbit/s 6 Mbit/s 27.5 TV (qualidade difusão, DVD) 576 × 720 576 × 360 25 img/s entrelaçadas 166 Mbit/s 3 Mbit/s 55 TV (gravação CD) 288 × 360 144 × 180 25 img/s progressivas 31 Mbit/s 1.15 Mbit/s 27 Videoconfer. 288 × 360 144 × 180 25 img/s progressivas 31 Mbit/s 2 Mbit/s 15.5 Videoconfer. 288 × 360 144 × 180 10 img/s progressivas 12.4 Mbit/s 384 kbit/s 32.3 Videotelefonia fixa 288 × 360 144 × 180 10 img/s progressivas 12.4 Mbit/s 64 kbit/s 194 Videotelefonia móvel (GSM) 144 × 180 72 × 90 5 img/s progressivas 1.6 Mbit/s 13 kbit/s 100
Audiovisual Compression: from Basics to Systems, Fernando Pereira
Interoperability as a Major Requirement: Standards to Assure that More is not Less ... Interoperability as a Major Requirement: Interoperability as a Major Requirement: Standards to Assure that More is not Less ... Standards to Assure that More is not Less ...
services where interoperability is a major requirement.
standards, notably audiovisual coding standards.
competition in the market between compatible products from different companies, standards must specify the minimum set
decoding process (not encoding process).
Audiovisual Compression: from Basics to Systems, Fernando Pereira
Standards: a Trade-off between Fixing and Inovating Standards: Standards: a Trade a Trade-
Inovating Encoder Decoder Normative Normative ! !
Audiovisual Compression: from Basics to Systems, Fernando Pereira
Video Coding Standards … Video Coding Standards … Video Coding Standards …
ITU-
T H.120 (1984) - Videoconference (1.5 - 2 Mbit/s)
ITU-
T H.261 (1988) – Audiovisual services (videotelephony and videoconference) at p× × × ×64kbit/s, p=1,…,30
ISO/IEC MPEG-
1 (1990)- CD-ROM Video
ISO/IEC MPEG-
2 also ITU ITU-
T H.262 (1993) – Digital TV
ITU-
T H.263 (1996) – PSTN and mobile video
ISO/IEC MPEG-
4 (1998) – Audiovisual objects, improved efficiency
ISO/IEC MPEG-
4 AVC also ITU ITU-
T H.264 ( (2003 2003) ) – Improved efficiency
Audiovisual Compression: from Basics to Systems, Fernando Pereira
Audiovisual Compression: from Basics to Systems, Fernando Pereira
Videotelephony and Videoconference Videotelephony and Videoconference Videotelephony and Videoconference
Personal (bidirectional) communications in real-time !
Audiovisual Compression: from Basics to Systems, Fernando Pereira
ITU-T H.320 Recommendation: Motivation ITU ITU-
T H.320 Recommendation: H.320 Recommendation: Motivation Motivation
The starting of the work towards Rec. H.320 and H.261 goes back to 1984 when it was acknowledged that:
notably videotelephony and videoconference.
lines as well as ISDN lines.
terminals for the digital lines mentioned above.
videoconference services, was already obsolete in terms of compression efficiency due to the fast development in the area of video compression.
Audiovisual Compression: from Basics to Systems, Fernando Pereira
Videotelephony and Videoconference: Main Features Videotelephony and Videoconference: Main Videotelephony and Videoconference: Main Features Features
(all nodes involved have the same similar features)
impacts
Audiovisual Compression: from Basics to Systems, Fernando Pereira
Audiovisual Compression: from Basics to Systems, Fernando Pereira
Audiovisual Compression: from Basics to Systems, Fernando Pereira
Recommendation H.261: Objectives Recommendation Recommendation H.261: H.261: Objectives Objectives
Efficient coding of videotelephony and videoconference sequences with a minimum acceptable quality using a bitrate from 40 kbit/s to 2 Mbit/s, targeting synchronous channels (ISDN) at p× × × ×64 kbit/s, with p=1,...,30. This is the first international video coding standard with meaningful adoption, thus introducing the notion of backward compatibility in video coding standards.
Audiovisual Compression: from Basics to Systems, Fernando Pereira
H.261: Signals to Code H.261: H.261: Signals to Code Signals to Code
chrominances, named CB and CR or U and V.
content at 29.97 image/s.
skipping 1, 2 or 3 images between each transmitted image.
Audiovisual Compression: from Basics to Systems, Fernando Pereira
H.261: Image Format H.261: H.261: Image Format Image Format
Two spatial resolutions are possible:
× × × 352 samples for luminance (Y) and 144 × × × × 176 samples for each chrominance (U,V) this means a 4:2:0 subsampling format, with ‘quincux’ positioning, progressive, 30 frame/s with a 4/3 form factor.
half spatial resolution in both directions this means 144 × × × × 176 samples for luminance and 72 × × × × 88 samples for each chrominance. All H.261 codecs must work with QCIF and some may be able to work also with CIF.
Audiovisual Compression: from Basics to Systems, Fernando Pereira
Groups Of Blocks (GOBs), Macroblocks and Blocks Groups Of Blocks Groups Of Blocks (GOBs), Macroblocks and (GOBs), Macroblocks and Blocks Blocks
1 2 3 4 5 6
GOB 1 GOB 2 GOB 3 GOB 4 GOB 7 GOB 6 GOB 8 GOB 9 GOB 5 GOB 11 GOB 10 GOB 12
The video sequence is spatially organized according to a hierarchical structure with 4 levels:
CIF CIF QCIF QCIF Y Y U U V V 4:2:0 4:2:0
Audiovisual Compression: from Basics to Systems, Fernando Pereira
Audiovisual Compression: from Basics to Systems, Fernando Pereira
H.261: Coding Tools H.261: H.261: Coding Tools Coding Tools
Predictive coding: sending differences and motion compensation
Transform coding (Discrete Cosine Transform, DCT)
Huffman entropy coding
Quantization of DCT coefficients
Audiovisual Compression: from Basics to Systems, Fernando Pereira
Audiovisual Compression: from Basics to Systems, Fernando Pereira
Temporal Prediction and Prediction Error Temporal Prediction and Prediction Error Temporal Prediction and Prediction Error
image may be represented using as reference a part of some preceding image, typically the previous one.
performance since it defines the amount of information to code and transmit, this means the energy of the error/difference signal called prediction error prediction error.
information/energy to transmit and thus
Audiovisual Compression: from Basics to Systems, Fernando Pereira
H.261 Temporal Prediction H.261 Temporal Prediction H.261 Temporal Prediction
H.261 temporal prediction includes 2 tools which have both the target to eliminate/reduce the temporal redundancy in the PCM video signal:
Sending the Differences Sending the Differences Motion Compensation Motion Compensation
Audiovisual Compression: from Basics to Systems, Fernando Pereira
Temporal Redundancy: Sending the Differences Temporal Redundancy: Temporal Redundancy: Sending the Sending the Differences Differences
The idea is that only the new information in the image (this means what changes from the previous image) is sent; the previous image works as an easy prediction of the current image.
There are no losses !
Audiovisual Compression: from Basics to Systems, Fernando Pereira
Computing the Differences: an Example Computing the Differences: an Example Computing the Differences: an Example
Image t Image t-1 Differences
Audiovisual Compression: from Basics to Systems, Fernando Pereira
Coding and Decoding ... Coding and Coding and Decoding Decoding ... ...
Audiovisual Compression: from Basics to Systems, Fernando Pereira
Eppur Si Muove … Eppur Eppur Si Si Muove Muove … …
Audiovisual Compression: from Basics to Systems, Fernando Pereira
Motion Estimation and Compensation Motion Motion Estimation and Compensation Estimation and Compensation
Motion estimation and compensation has the target to improve the temporal predictions for each image zone by detecting, estimating and compensating the motion in the image.
so called block matching is the most used technique.
usage of motion compensation for each MB is optional and decided by the encoder. Motion estimation implies a very high computational effort. This justifies the usage of fast motion estimation methods which try to decrease the complexity compared to full search without much quality losses.
Audiovisual Compression: from Basics to Systems, Fernando Pereira
Temporal Redundancy: Motion Estimation Temporal Redundancy: Temporal Redundancy: Motion Estimation Motion Estimation
t t
Audiovisual Compression: from Basics to Systems, Fernando Pereira
Search, Where ? Search, Where ? Search, Where ?
Searching area
Image to code Reference image
Audiovisual Compression: from Basics to Systems, Fernando Pereira
Motion Vectors at Different Spatial Resolutions Motion Vectors Motion Vectors at Different at Different Spatial Resolutions Spatial Resolutions
Audiovisual Compression: from Basics to Systems, Fernando Pereira
MBs to Code and Prediction MBs MBs to Code and Prediction MBs MBs to Code and Prediction MBs
Reference image Reference image Current image Current image
Audiovisual Compression: from Basics to Systems, Fernando Pereira
Motion Compensation: an Example Motion Compensation: an Example Motion Compensation: an Example
Image t Image t-1
Differences WITH motion comp Motion vectors
Audiovisual Compression: from Basics to Systems, Fernando Pereira
The 3- Steps Motion Estimation Algorithm The 3 The 3-
Steps Motion Estimation Algorithm
Fast motion estimation algorithms offer lower complexity at the cost of some quality decrease since predictions are less optimal and thus the prediction error is higher !
Audiovisual Compression: from Basics to Systems, Fernando Pereira
Motion Vectors at Different Spatial Resolutions Motion Vectors Motion Vectors at Different at Different Spatial Resolutions Spatial Resolutions
Audiovisual Compression: from Basics to Systems, Fernando Pereira
Motion Compensation Decision Characteristic Motion Compensation Decision Characteristic Motion Compensation Decision Characteristic
db db – – difference block difference block dbd dbd – – displaced block difference displaced block difference
X X
Audiovisual Compression: from Basics to Systems, Fernando Pereira
H.261 Motion Estimation H.261 Motion Estimation H.261 Motion Estimation
encoder so desires).
pels, in the vertical and horizontal directions, only the integer values.
coded) image are valid.
luminance blocks in the MB. The chrominance motion vector is computed by dividing by 2 and truncating the luminance motion vector.
components means the prediction must be made using the samples in the previous image, spatially located to the right and below the samples to be predicted.
Audiovisual Compression: from Basics to Systems, Fernando Pereira
H.261 Motion Vectors Coding H.261 Motion Vectors Coding H.261 Motion Vectors Coding
MBs, each motion vector is differentially coded as the difference between the motion vector of the actual MB and its prediction, this means the motion vector of the preceding MB.
no redundancy is likely to be present, notably when:
to the actual MB
not use motion compensation
Audiovisual Compression: from Basics to Systems, Fernando Pereira
VLC Coding Table for (Differential) Motion Vectors VLC Coding Table VLC Coding Table for (Differential) for (Differential) Motion Vectors Motion Vectors
Audiovisual Compression: from Basics to Systems, Fernando Pereira
Inter Versus Intra Coding Inter Versus Intra Coding Inter Versus Intra Coding
In H.261, the MBs are coded either in Inter or Intra coding modes:
temporal redundancy; may imply the usage or not of motion estimation.
temporal redundancy; no temporal predictive coding is used in this case (absolute coding like in JPEG is used).
Audiovisual Compression: from Basics to Systems, Fernando Pereira
Audiovisual Compression: from Basics to Systems, Fernando Pereira
After Time, the Space After Time, the Space After Time, the Space
Actual image Prediction image, motion compensated +
Prediction error DCT Transform
Audiovisual Compression: from Basics to Systems, Fernando Pereira
Transform Coding Transform Coding Transform Coding
Transform coding involves the division of the image in N× × × ×N blocks to which the transform is applied, producing blocks with N× × × ×N coefficients.
A transform is formally defined through the formulas with the direct and inverse transforms:
F(u,v) = Σ Σ Σ Σi=0
N-1 Σ
Σ Σ Σ j=0
N-1 f(i,j) A(i,j,u,v)
f(i,j) = Σ Σ Σ Σu=0
N-1 Σ
Σ Σ Σ v=0
N-1 F(u,v) B(i,j,u,v)
where f(i,j) – Input signal (in space) A (i,j,u,v) – Direct transform basis functions F(u,v) – Transform coefficients B (i,j,u,v) – Inverse transform basis functions
Audiovisual Compression: from Basics to Systems, Fernando Pereira
The Transform Coefficients The Transform Coefficients The Transform Coefficients
Audiovisual Compression: from Basics to Systems, Fernando Pereira
Discrete Cosine Transform (DCT) Discrete Cosine Transform Discrete Cosine Transform (DCT) (DCT)
The DCT is one of the sinusoidal transforms which means its basis functions are sampled sinusoidal functions. The DCT is undoubtedly the most used transform in image and video coding since its performance is close to the KLT compacting performance for signals with high correlation and there are fast implementation solutions available.
= − =
=
1 1
2 1 2 2 1 2 2
N j N k
N k v N j u k j f v C u C N v u F ) ( cos ) ( cos ) , ( ) ( ) ( ) , ( π π
= − =
=
1 1
2 1 2 2 1 2 2 N
u N v
N k v N j u v u F v C u C N k j f π π ) ( cos ) ( cos ) , ( ) ( ) ( ) , (
Audiovisual Compression: from Basics to Systems, Fernando Pereira
Bidimensional DCT Basis Functions (N=8) Bidimensional Bidimensional DCT Basis Functions (N=8 DCT Basis Functions (N=8) )
Audiovisual Compression: from Basics to Systems, Fernando Pereira
The DCT Transform in H.261 The DCT Transform The DCT Transform in in H.261 H.261
the DCT is applied to blocks with 8× ×8 samples 8 samples. This value results from a trade-off between the exploitation of the spatial redundancy and the computational complexity.
selected using non-
normative thresholds thresholds allowing the consideration of psycho visual criteria in the coding process, targeting the maximization of the subjective quality.
to transmit for each block are quantized are quantized.
coefficients’ matrix and the human visual system sensibility is different for the various frequencies, the quantized coefficients are the quantized coefficients are zig zig-
zag scanned scanned to assure that more important coefficients are always transmitted before less important ones.
Audiovisual Compression: from Basics to Systems, Fernando Pereira
H.261 Quantization H.261 Quantization H.261 Quantization
all even values between 2 and 62 (32 quantizers available).
coefficients are quantized with the same quantization step with the exception of the DC coefficient for Intra MBs which are always quantized with step 8.
regeneration values for the quantized coefficients but not the decision values which may be selected to implement different quantization characteristics, uniform or not.
Example quantization characteristic
Audiovisual Compression: from Basics to Systems, Fernando Pereira
Serializing the DCT Coefficients Serializing the DCT Serializing the DCT Coefficients Coefficients
coefficients requires to send the decoder two types of information about the coefficients: their position and amplitude.
its position and amplitude is represented using a bidimensional symbol called
(run, level)
where the run indicates the number of null coefficients before the coefficient under coding, and the level indicates the quantized value of the coefficient.
Audiovisual Compression: from Basics to Systems, Fernando Pereira
Audiovisual Compression: from Basics to Systems, Fernando Pereira
Statistical Redundancy: Entropy Coding Statistical Redundancy: Statistical Redundancy: Entropy Coding Entropy Coding
Entropy coding CONVERTS SYMBOLS IN BITS ! Using the statistics of the symbols to transmit to achieve additional (lossless) compression by allocating in a clever way bits to the input symbol stream.
Audiovisual Compression: from Basics to Systems, Fernando Pereira
Huffman Coding Huffman Coding Huffman Coding
Huffman coding is one of the entropy coding tools which allows to exploit the fact that the symbols produced by the encoder do not have equal probability.
(in bits) is inversely proportional to its probability.
channel is available.
sensibility to channel errors.
Audiovisual Compression: from Basics to Systems, Fernando Pereira
Audiovisual Compression: from Basics to Systems, Fernando Pereira
The H.261 Symbolic Model The H.261 Symbolic Model The H.261 Symbolic Model
A video sequence is represented as a set of images structured in macroblocks, each of them represented with motion vectors and/or (Intra or Inter coded) DCT coefficients for 8×8 blocks.
Symbol Generator (Model) Entropy Coder
Original Video Symbols Bits
Audiovisual Compression: from Basics to Systems, Fernando Pereira
Encoder: the Winning Cocktail ! Encoder: Encoder: the Winning the Winning Cocktail Cocktail ! !
Originals DCT
Quantiz. Symbols Gener.
Entropy coder Entropy coder
Inverse Quantiz.
Inverse DCT Buffer Motion det./comp.
+ +
Previous frame
Audiovisual Compression: from Basics to Systems, Fernando Pereira
Decoder: the Slave ! Decoder: the Slave ! Decoder: the Slave !
Buffer Huffman decoder Motion comp. Demux. IDCT
+
Data Data
Audiovisual Compression: from Basics to Systems, Fernando Pereira
Output Buffer Output Buffer Output Buffer
The production of bits by the encoder is highly non-uniform in time essentially because:
To adapt the variable bitrate flow produced by the encoder to th To adapt the variable bitrate flow produced by the encoder to the e constant bitrate flow transmitted by the channel, an output constant bitrate flow transmitted by the channel, an output buffer is used, which adds some delay. buffer is used, which adds some delay.
Audiovisual Compression: from Basics to Systems, Fernando Pereira
Bitrate Control Bitrate Control Bitrate Control
The encoder must efficiently control the way the available bits are spent in order to maximize the decoded quality for the synchronous bitrate/channel available.
various tools are available:
The bitrate control strategy has a huge impact on the video quality that may be achieved with a certain bitrate !
Audiovisual Compression: from Basics to Systems, Fernando Pereira
Quantization Step versus Buffer Fullness Quantization Step versus Buffer Fullness Quantization Step versus Buffer Fullness
The bitrate control solution recognized as most efficient, notably in terms of the granularity and frequency of the control, controls the quantization step as a function of the output buffer fullness.
Encoder
Output buffer
Video sequence Binary flow Quantization step control
Audiovisual Compression: from Basics to Systems, Fernando Pereira
The Importance of Well Choosing ! The Importance of Well Choosing ! The Importance of Well Choosing !
To well exploit the redundancy and irrelevancy in the video sequence, the encoder has to adequately select:
characteristics;
vector and DCT coefficients. While the encoder has the mission to take important decisions and make critical choices, the decoder is a ‘slave’, limited to follow the ‘orders’ sent by the encoder; decoder intelligence is only shown for error concealment.
Audiovisual Compression: from Basics to Systems, Fernando Pereira
Macroblock Classification Macroblock Classification Macroblock Classification
level that the encoder selects the coding tools to use.
content and thus MB; it is important that, for each MB, the right coding tools are selected.
encoder to select the best tools for each MB; MBs are thus classified following the tools used for their coding.
if also temporal redundancy is exploited, MBs are INTER coded.
Audiovisual Compression: from Basics to Systems, Fernando Pereira
Macroblock Classification Table Macroblock Classification Table Macroblock Classification Table
Audiovisual Compression: from Basics to Systems, Fernando Pereira
Hierarchical Information Structure Hierarchical Information Structure Hierarchical Information Structure
Audiovisual Compression: from Basics to Systems, Fernando Pereira
Coding Syntax: Image and GOB Levels Coding Syntax: Image and GOB Levels Coding Syntax: Image and GOB Levels
Audiovisual Compression: from Basics to Systems, Fernando Pereira
Coding Syntax: MB and Block Levels Coding Syntax: MB and Block Levels Coding Syntax: MB and Block Levels
Audiovisual Compression: from Basics to Systems, Fernando Pereira
Error Protection for the H.261 Binary Flow Error Protection for the H.261 Binary Flow Error Protection for the H.261 Binary Flow
using a BCH (511,493) - Bose-Chaudhuri-Hocquenghem – block coding.
decoder is optional.
g (x) = (x g (x) = (x9
9+ x
+ x4
4+ x) ( x
+ x) ( x9
9+ x
+ x6
6+ x
+ x4
4+ x
+ x3
3+ 1)
+ 1)
Audiovisual Compression: from Basics to Systems, Fernando Pereira
Error Protection for the H.261 Binary Flow Error Protection for the H.261 Binary Flow Error Protection for the H.261 Binary Flow
The final video signal stream structure (multiframe with 512× × × ×8 = 4096 bits):
00011011 00011011
When decoding, realignment is
reception of 3 alignment sequences (S1S2 ...S8).
S1 S2 S7 S8
Transmission
S1
Video bits Parity bits (1) (493) (18)
1
Code bits Stuffing bits (1's) (1) (1) (492) (492) S1S2S3S4S5...S8 – Alignment sequence
Audiovisual Compression: from Basics to Systems, Fernando Pereira
Error Concealment Error Concealment Error Concealment
errors may end at the source decoder.
syntactical and semantic inconsistencies.
imply:
through post-processing.
Audiovisual Compression: from Basics to Systems, Fernando Pereira
Error Concealment and Post-Processing Examples Error Concealment and Post Error Concealment and Post-
Processing Examples Examples
Audiovisual Compression: from Basics to Systems, Fernando Pereira
Final Comments Final Comments Final Comments
standard with significant adoption.
established legacy and backward compatibility requirements which influenced the next standards to come, notably in terms of technology selected.
H.261.
the-art on video coding (remind this standard is from 1990).
Audiovisual Compression: from Basics to Systems, Fernando Pereira
Bibliography Bibliography Bibliography
Artech House, 1996
Architectures, Vasudev Bhaskaran and Konstantinos Konstantinides, Kluwer Academic Publishers, 1995
2001
Tsuhan Chen, Marcel Dekker, Inc., 2000