Multimedia Data Compression - PowerPoint PPT Presentation

1 / 50
About This Presentation
Title:

Multimedia Data Compression

Description:

Multimedia Data Compression Mee Young Sung University of Incheon Department of Computer Science & Engineering mysung_at_incheon.ac.kr Lossless Compression Algorithms ... – PowerPoint PPT presentation

Number of Views:123
Avg rating:3.0/5.0
Slides: 51
Provided by: KDHs
Category:

less

Transcript and Presenter's Notes

Title: Multimedia Data Compression


1
Multimedia Data Compression
  • Mee Young Sung
  • University of Incheon
  • Department of Computer Science Engineering
  • mysung_at_incheon.ac.kr

2
  1. Lossless Compression Algorithms
  2. Introduction
  3. Basics of Information Theory
  4. Run-Length Coding
  5. Variable-Length Coding(VLC)
  6. Shannon-Fano Algorithm
  7. Huffman Coding
  8. Adaptive Huffman Coding
  9. Dictionary-Based Coding
  10. Arithmetic Coding
  11. Lossless Image Compression
  12. Differential Coding of Images
  13. Differential Coding of Images
  14. Lossless JPEG

3
  • Lossy Compression Algorithms
  • Introduction
  • Distortion Measures
  • The Rate-Distortion Theory
  • Quantization
  • Uniform Scalar Qunantization
  • Nonuniform Saclar Qunantization
  • Vector Qunantization
  • Quantization
  • Discrete Cosine Transform(DCT)
  • Karhunen-Loève Transform
  • Wavelet-Based Coding
  • Introduction
  • Continuous Wavelet Transform
  • Discrete Wavelet Transform
  • Wavelet Packets
  • Embedded Zerotree of Wavelet Coefficients
  • The Zerotree Data Structure
  • Successive Approximation Quantization

4
  • Image Compression Standard
  • The JPEG Standard
  • Main Steps in JPEG Image Compression
  • JPEG Modes
  • A Glance at the JPEG Bitstream
  • The JPEG2000 Standard
  • Main Steps of JPEG2000 Image Compression
  • Adapting EBCOT to JPEG2000
  • Region-of-Interest Coding
  • Comparison of JPEG and JPEG2000 Performance
  • The JPEG-LS Standard
  • Prediction
  • Context Determination
  • Residual Coding
  • Near-Lossless Mode
  • Bilevel Image Compression Standard
  • The JBIG Standard
  • The JBIG2 Standard

5
  • Basic Video Compression Techniques
  • Introduction to Video Compression
  • Video Compression Based on Motion Compensation
  • Search for Motion Vectors
  • Sequential Search
  • 2D Logarithmic Search
  • Hierarchical Search
  • H.261
  • Intra-Frame (I-Frame) Coding
  • Inter-Frame (P-Frame) Predictive Coding
  • Quantization in H.261
  • H.261 Encoder and Decoder
  • A Glance at the H.261 Video Bitstream Syntax
  • H.263
  • Motion Compensation in H.263
  • Optional H.263 Coding Modes
  • H.263 and H.263

6
  • MPEG Video Coding I MPEG-1 and 2
  • Overview
  • MPEG-1
  • Motion Compensation in MPEG-1
  • Other Major Differences form H.261
  • MPEG-1 Video Bitstream
  • MEPG-2
  • Supporting Interlaced Video
  • MPEG-2 Scalabilities
  • Other Major Differences form MPEG-1

7
  • MPEG Video Coding II MPEG-4, 7, and Beyond
  • Overview of MPEG-4
  • Object-Based Visual Coding in MPEG-4
  • VOP-Based Coding vs. Frame-Based Coding
  • Motion Compensation
  • Texture Coding
  • Shape Coding
  • Static Texture Coding
  • Sprite Coding
  • Global Motion Compensation
  • Synthetic Object Coding in MPEG-4
  • 2D Mesh Object Coding
  • 3D Model-based Coding
  • MPEG-4 Object Types, Profiles and Levels
  • MPEG-4 Part10/H.264
  • Core Features
  • Baseline Profile Features
  • Main Profile Features
  • Extended Profile Features

8
  • Basic Audio Compression Techniques
  • ADPCM in Speech Coding
  • ADPCM
  • G.726 ADPCM
  • Vocoders
  • Phase Insensitivity
  • Channel Vocoder
  • Formant Vocoder
  • Linear Predictive Coding
  • CELP
  • Hybrid Excitation Vocoders

9
  • MPEG Audio Compression
  • Psychoacoustics
  • Equal-Loudness Relations
  • Frequency Masking
  • Temporal Masking
  • MPEG Audio
  • MPEG Layers
  • MPEG Audio Strategy
  • MPEG Audio Compression Algorithm
  • MPEG-2 AAC(Advanced Audio Coding)
  • MPEG-4 Audio
  • Other Commercial Audio Codecs
  • The Future MPEG-7 and MPEG-21

10
Run-Length Coding
  • ???
  • 00 ... 00100 ... 001100 ... 00100
    ... 001100 ... 00 89 ??
  •                     ?      
    ?                       
  • 0??????  14        9        20       
    30         9
  •                                   0????           
             0????
  • ???? ???? ??
  • Run length(??) 1110 1001 0000 1111 0101 1111 1111
    0000 0000 1001 40??
  • Run length(??)  14    9   0    15    5   15  
    15    0    0   9    


11
Huffman Coding
  • Encoding for Huffman Algorithm
  • A bottom-up approach
  • 1. Initialization Put all nodes in an OPEN list,
    keep it sorted at all times (e.g., ABCDE).
  • 2. Repeat until the OPEN list has only one node
    left
  • (a) From OPEN pick two nodes having the lowest
    frequencies/probabilities, create a parent node
    of them. (b) Assign the sum of the children's
    frequencies/probabilities to the parent node and
    insert it into OPEN. (c) Assign code 0, 1 to the
    two branches of the tree, and delete the children
    from OPEN.

Symbol Count log2(1/pi) Code Subtotal ( of bits)
A 15 1.38 0 15
B 7 2.48 100 21
C 6 2.70 101 18
D 6 2.70 110 18
E 5 2.96 111 15

12
Huffman Coding
  • ?? ??? ??? ?? ???? ??? ???? ??? ????,  ?? ??? ??
    ?????? ?? ??
  • ?? ?? ???? ???? ? ??? ?? ?? ???? ??, ???? ???
    ???? ?? ???? ??? ?? ?? ???, ??? ???? ??? ?? ??
    ??? ???? ??? ???? ???? ??
  • ???? ???? ??? ????? ???? ?? ?? ?? ???? ??? ???
    ??? ? ??
  • ?? ?? ??? ??? ??


13
Fourrier Transform
14
Reinforcement and Interference
15
Complex Wave Fundamental and Spectral Frequencies
16
Line Spectrum
17
Fundamental Frequency in a Line Spectrum
18
DCT
  • Discrete Cosine Transform (DCT)
  • Inverse Discrete Cosine Transform (IDCT)

19
Example 1
1D DCT
20
Example 2
21
Example 3
22
Example 4
23
Example of 1D IDCT
24
(No Transcript)
25
(No Transcript)
26
(No Transcript)
27
(No Transcript)
28
(No Transcript)
29
JPEG
  • Joint Photographic Expert Group
  • Motivation
  • The compression ratio of lossless methods (e.g.,
    Huffman, Arithmetic, LZW) is not high enough for
    image and video compression, especially when the
    distribution of pixel values is relatively flat.
  • JPEG uses transform coding, it is largely based
    on the following observations
  • Observation 1 A large majority of useful image
    contents change relatively slowly across images,
    i.e., it is unusual for intensity values to alter
    up and down several times in a small area, for
    example, within an 8 x 8 image block. Translate
    this into the spatial frequency domain, it says
    that, generally, lower spatial frequency
    components contain more information than the high
    frequency components which often correspond to
    less useful details and noises.
  • Observation 2 Pshchophysical experiments suggest
    that humans are more receptive to loss of higher
    spatial frequency components than loss of lower
    frequency components.

30
JPEG (DCT)
  • Discrete Cosine Transform (DCT)
  • Inverse Discrete Cosine Transform (IDCT)

31
JPEG
  • Encoding

32
JPEG
  • Major Steps
  • DCT (Discrete Cosine Transformation)
  • Quantization
  • Zigzag Scan
  • DPCM on DC component
  • RLE on AC Components
  • Entropy Coding

33
JPEG (DCT)
  • The 64 (8 x 8) DCT basis functions

34
JPEG (DCT)
  • Computing the DCT
  • Factoring reduces problem to a series of 1D DCTs

35
JPEG (Quantization)
  • F'u, v round ( Fu, v / qu, v ). Why? --
    To reduce number of bits per sample
  • Example 101101 45 (6 bits). qu, v 4 --gt
    Truncate to 4 bits 1011 11.
  • Quantization error is the main source of the
    Lossy Compression.
  • Uniform Quantization
  • Each Fu,v is divided by the same constant N.
  • Non-uniform Quantization -- Quantization Tables
  • Eye is most sensitive to low frequencies (upper
    left corner), less sensitive to high frequencies
    (lower right corner)
  • The numbers in the above quantization tables can
    be scaled up (or down) to adjust the so called
    quality factor.
  • Custom quantization tables can also be put in
    image/scan header.

36
JPEG (Quantization)
  • The Luminance Quantization Table q(u, v)
    The Chrominance Quantization Table q(u, v)

Eye Sensitivity
37
JPEG (Zig-Zag Scan)
  • Zig-zag Scan
  • Why? -- to group low frequency coefficients in
    top of vector.
  • Maps 8 x 8 to a 1 x 64 vector

38
JPEG (DPCM on DC component )
  • DPCM (Differential Pulse Code Modulation)
  • DC component is large and varied, but often close
    to previous value.
  • Encode the difference from previous 8 x 8 blocks
    DPCM
  • DPCM ???/??? ?
  • ??? ????? ????? ????? ?? ???, ?? ?? ??? ????? ?
    ??? ??? ???? ????? ??

(a)??? ??? ??? 14 19 25 36 43 55 66 52 48 34
(b) DPCM??? ??? 14 5 6 11 7 12 11 -14 4 -14
(c)??? ??? 14 19 25 36 43 55 66 52 48 34
39
JPEG (RLE on AC components )
  • RLE (Run Length Encode)
  • 1 x 64 vector has lots of zeros in it
  • Keeps skip and value, where skip is the number of
    zeros and value is the next non-zero component.
  • Send (0,0) as end-of-block sentinel value
  • Zig-Zag scanned ACs

40
JPEG (Entropy Coding )
  • Categorize DC values into SIZE (number of bits
    needed to represent) and actual bits.
  • Example if DC value is 4, 3 bits are needed.
  • Send off SIZE as Huffman symbol, followed by
    actual 3 bits.
  • For AC components two symbols are used Symbol_1
    (skip, SIZE), Symbol_2 actual bits. Symbol_1
    (skip, SIZE) is encoded using the Huffman coding,
    Symbol_2 is not encoded.
  • Huffman Tables can be custom (sent in header) or
    default.

Size Amplitude 0 0 1 -1,1 2 -3,-2,2,3 3
-7,-6,-5,-4,4,5,6,7
41
JPEG Example
42
JPEG Example
43
H. 261
  • Developed by CCITT (Consultative Committee for
    International Telephone and Telegraph) in
    1988-1990
  • Designed for videoconferencing, video-telephone
    applications over ISDN telephone lines. Bit-rate
    is p x 64 Kb/sec, where p ranges from 1 to 30.
  • Frame types are CCIR 601 CIF (352 x 288) and QCIF
    (176 x 144) images with 420 subsampling.
  • Two frame types Intra-frames (I-frames) and
    Inter-frames (P-frames) I-frame provides an
    accessing point, it uses basically JPEG.
  • P-frames use "pseudo-differences" from previous
    frame ("predicted"), so frames depend on each
    other.

44
Subsampling
45
H.261(Intra-frame Coding)
  • Macroblocks are 16 x 16 pixel areas on Y plane of
    original image. A macroblock usually consists of
    4 Y blocks, 1 Cr block, and 1 Cb block.
  • Quantization is by constant value for all DCT
    coefficients (i.e., no quantization table as in
    JPEG).

46
H.261(Inter-frame (P-frame) Coding )
  • Previous image is called reference image, the
    image to encode is called target image.
  • Points to emphasize
  • The difference image (not the target image
    itself) is encoded.
  • Need to use the decoded image as reference image,
    not the original.
  • We're using "Mean Absolute Error" (MAE) to decide
    best block. Can also use "Mean Squared Error"
    (MSE) sum(EE)/N

47
H.263
  • H. 263 is a new improved standard for low
    bit-rate video, adopted in March 1996. As H.
    261, it uses the transform coding for
    intra-frames and predictive coding for
    inter-frames.
  • Advanced Options
  • Half-pixel precision in motion compensation
  • Unrestricted motion vectors
  • Syntax-based arithmetic coding
  • Advanced prediction and PB-frames
  • In addition to CIF and QCIF, H. 263 could also
    support SQCIF, 4CIF, and 16CIF. The following is
    a summary of video formats supported by H. 261
    and H. 263

48
MPEG
  • What is MPEG ?
  • "Moving Picture Coding Experts Group",
    established in 1988 to create standard for
    delivery of video and audio.
  • MPEG-1 Target VHS quality on a CD-ROM or Video
    CD (VCD) (352 x 240 CD audio _at_ 1.5 Mbits/sec)
  • Standard had three parts Video, Audio, and
    System (control interleaving of streams)
  • MPEG Video
  • Problem some macroblocks need information not in
    the previous reference frame. Example The
    darkened macroblock in Current frame does not
    have a good match from the Previous frame, but it
    will find a good match in the Next frame.

49
MPEG
  • MPEG solution add third frame type
    bidirectional frame, or B-frame In B-frames,
    search for matching macroblocks in both past and
    future frames.
  • Typical pattern is IBBPBBPBB IBBPBBPBB IBBPBBPBB
    Actual pattern is up to encoder, and need not be
    regular

50
?? ???
  • http//www.cs.sfu.ca/mmbook/demos.htm
  • http//www.cs.sfu.ca/CC/365/li/material/misc/demos
    .html
Write a Comment
User Comments (0)
About PowerShow.com