1. VIDEO
COMPRESSION
TECHNIQUES
BY- SAHIL JAIN
SUBMITTED TO- ANIL JAIN SIR

2. Name : Sahil Jain
Roll No. : 104
Branch : ECE
Year : 3rd

3. CONTENTS
 Overview
• A Little Theory
• Achieving Compression
• Various Algorithms
• Spatial Compression
• Spectral Compression
• Steps in Image Compression
• Temporal Compression
• Video Coding and Frames
• Motion Compensated Prediction
• Motion Estimation
• Block Matching Summary
• Basic VC Architecture
• Video Encoder
• Video Decoder
• Using Video Compression Standards
• Current Video Compression Standards
• Video Coding Standardization Organizations
• Dynamics of Video Standardization Process
• Video Compression Standards
• Scope of Development
• References and Further Reading

4. OVERVIEW
 To reduce quantity of data used to represent digital video images.
•Saves space and bandwidth
• Saves energy
• Increases portability
• Reduce cost
 Eg.- 720 X 1280 pixels/frame, progressive scanning @60
frames/sec
(720X 1280 ppf)(60 fps)(3 colors/pixel)(8 bits/color) =
1.3Gb/sec
• 20 Mbps HD channel bandwidth
• Requires compression by a factor of 70(equivalent to .35
bits/ pixel)

5. Example

6. A Little Theory
 Video – It is a 3-D array of color pixels.
• Two of the dimensions serve as the spatial domain for moving
pictures
• One dimension serves as the time domain
 Data Frame – Set of all pixels that corresponds to single time
moment.
 Human eye
• Not responsive to every detail
• Quantization
• Smoothening
• More responsive to brightness than chrominance

7. Achieving Compression
 Reduce redundancy and irrelevancy.
 Sources of Redundancy
• Temporal: Adjacent frames highly correlated
• Spatial: Nearby pixels are often correlated
• Color space: RGB components are correlated among themselves
• Relatively straight to exploit
 Sources of Irrelevancy
• Perceptually unimportant information
• Difficult to model and exploit

8. Various Algorithms
4 Major ways to compress videos
 DCT(Discrete Cosine Transform)
• Samples images at regular intervals
• Analyze frequency components present in sample
• Discard unimportant frequencies from point of view of human
eye
 Vector Quantization(VQ)
• It looks at an array of data rather than individual values and
than averaging what it perceives
• Compresses the found redundant data keeping the desired
object
contd…

9.  Fractal compression(FC)
• Form of vector quantization.
• Finds self-similar section of particular image, than uses fractal
algorithm to create the sections
 Discrete Wavelet Transform(DWT)
• Mathematically transform image into frequency components
• Process is performed on entire frame, the end result is very
effective hierarchical representation of an image
• Every layer represents a frequency band

10. Spatial Compression
 Removing or reordering information about field of color pixels to
conserve space
 Neighboring pixels will have nearly the same brightness and color
values
 Instead of sending the same number for each and every
sample, one number could be sent representing a block of sample
points in an area where the information content remain same

11. Spectral Compression
 Human eye is much better in distinguishing luminance than
chrominance.
 This can be used as advantage in conveying color information as
there is less precision required – a higher level of precision would
thus be „ saved‟.
 Fewer samples required to convey color information – fewer
samples-> lesser bandwidth required

12. Steps in Image
Compression
 If the color is represented in RGB mode, translate it to YCrCb mode
 Divide the file into 8X8 blocks(group of 8 pixels = 1 block).
 Transform the pixel information from the spatial domain into the
frequency domain with the Discrete Cosine Transform.
 Quantize the resulting values by dividing each coefficient by an integer
value and rounding off to the nearest integer.
 Look at the resulting coefficients in a zigzag order. Do a run-length
encoding of the coefficients ordered in this manner. Follow by Huffman
coding.
 When conversion is done from RGB to YCrCb, it makes 4:4:4
format, but 2 color components are discarded. Thus bandwidth is
reduced by 50%

13. 1 3 9 1 4 4 1 4 9 1 5 3 DC component
1 4 4 1 5 1 1 5 3 1 5 6
D 1260 -1 -1 2 -5
1 5 0 1 5 5 1 6 0 1 6 3 C -2 3 -1 7 -6 -3
1 5 9 1 6 1 1 6 2 1 6 0 T -1 1 -9 -2 2
-7 -2 0 1
Orignal Image
Quantize
AC components
79 0 -1 0
ZigZag -2 -1 0 0
79 0 -2 -1 -1 -1 0 0 -1 0 0 0 0 0 0 0
-1 -1 0 0
0 0 0 0
0 79
Run-length 1 -2
code 0 -1 Huffman 10011011100011...
code
0 -1
0 -1
coded bitstream < 10 bits (0.55 bits/pixel)
2 -1
0 0

14. Temporal Compression
 Redundancy between successive frames is known as temporal
compression.
 Only changes from one frame to the next are encoded as often as
large number of pixels will be same on series of frames.
 This type of compression relies on keyframes.
 Keyframes stores still images which are used for frame
differencing.

15. Temporal Processing
 Usually high frame rate: Significant temporal redundancy.
 Possible representations along temporal dimension.
• Transform/subband methods
• Good for textbook case of constant velocity uniform global
motion.
• Inefficient for non uniform motion, i.e. real-world motion.
• Requires large number of frame stores.
• Leads to delay (Memory cost may also be an issue).
• Predictive methods
• Good performance using only 2 frame stores.
• However, simple frame differencing in not enough…

16. Video Coding and Frames
 Goal: Exploit the temporal redundancy
 Predict current frame based on previously coded frames
 Three types of coded frames:
• I-frame: Intra-coded frame, coded independently of all
other frames
• P-frame: Predictively coded frame, coded based on
previously coded frame
• B-frame: Bi-directionally predicted frame, coded based on
both previous and future coded frames

17. Examples

18. Motion Compensated Prediction
 Simple frame differencing fails when there is motion
 Must account for motion
• Motion-compensated (MC) prediction
 MC-prediction generally provides significant improvements
 Questions:
• How can we estimate motion?
• How can we form MC-prediction?

19. Motion Estimation
 Ideal situation:
• Partition video into moving objects
• Describe object motion
• Generally very difficult
 Practical approach: Block-Matching Motion Estimation
• Partition each frame into blocks, e.g. 16x16 pixels
• Describe motion of each block
• No object identification required
• Good, robust performance

20. Examples of ME (P and B Frame

21. Block Matching ME Summary
 Issues:
• Block size?
• Search range?
• Motion vector accuracy?
• Motion typically estimated only from luminance
 Advantages:
• Good, robust performance for compression
• Resulting motion vector field is easy to represent (one MV per block)
and useful for compression
• Simple, periodic structure, easy VLSI implementations
 Disadvantages:
• Assumes translational motion model Breaks down for more complex
motion
• Often produces blocking artifacts (OK for coding with Block DCT)

22. Basic VC Architecture
 Exploiting the redundancies:
• Temporal: MC-prediction (P and B frames)
• Spatial: Block DCT
• Spectral: Color space conversion
 Scalar quantization of DCT coefficients
 Zigzag scanning, run length and Huffman coding of the nonzero
quantized DCT coefficients

23. Video Encoder

24. Video Decoder

25. Using Standards in Video Compression
 Motivation for Standards
• Ensuring interoperability: Enabling communication between
devices made by different manufacturers
• Promoting a technology or industry
• Reducing costs
 What do the Standards imply?
• Just the bitstream syntax and the decoding process(e.g. use
IDCT, but not how to implement the IDCT)
• Enables improved encoding & decoding strategies to be
employed in a standard-compatible manner

26. Current Video Compression Standards
STANDARD APPLICATION BIT RATE
JPEG Continuous-tone still-image Variable
compression
H.261 Video telephony and teleconferencing p x 64 kb/s
over ISDN
MPEG-1 Video on digital storage media (CD- 1.5 Mb/s
ROM)
MPEG-2 Digital Television > 2 Mb/s
H.263 Video telephony over PSTN < 33.6 kb/s
MPEG-4 Object-based coding, synthetic Variable
content, interactivity
H.264 From Low bitrate coding to HD Variable
encoding, HD-DVD, Surveillance,
Video conferencing.

27. Video Coding Standardization Organizations
Two key Organizations:
 ITU-T (Video Coding Experts group, VCEG)
• International Telecommunications Union – Telecommunications
Standardization Sector (ITU-T, a United Nations
Organization, formerly CCITT)
 ISO/IEC Moving Picture Experts Group (MPEG)
• International Standardization Organization and International
Electrotechnical Commission

28. Dynamics of Video Compression
Standardization
 VCEG is older and more focused on conventional (esp. low-delay)
video coding goals (e.g. good compression and packet-loss/error
resilience)
 MPEG is larger and takes on more ambitious goals (e.g. “object
oriented video”, “synthetic-natural hybrid coding”, and digital
cinema)
 Sometimes the major organizations team up (e.g. ISO, IEC and
ITU teamed up for both MPEG-2 and JPEG)
contd…

29.  Relatively little industry consortium activity (DV and organizations
that tweak the video coding standards in minor ways, such as
DVD, 3GPP, 3GPP2, SMPTE, IETF, etc.)
 Growing activity for internet streaming media outside of formal
standardization (e.g., Microsoft, Real Networks, Quicktime)

30. MPEG-I
 MPEG-1, the first lossy compression scheme developed by the MPEG
committee.
 Used for CD-ROM video compression and as part of early Windows
Media players.
 Uses DCT algorithm.
 For slow moving frames e.g. In talk shows greater compression is
achieved.
 For fast moving frames e.g. In sports channels lesser compression is
achieved.
 The current wildly popular MP3 (MPEG-1, Part 3) audio standard is
actually the audio compression portion of the MPEG-1 standard and
provides about 10:1compression of audio files at reasonable quality.

31. MPEG-II
 Evolved to meet the needs of compressing higher quality video.
 used in today‟s video DVDs and digital broadcasts.
 It uses bit rates typically ranging from 5 to 8 Mbits/s.
 Uses DCT transforms but it also provides support for interlaced
video.
 MPEG-2 is also the current standard for (HDTV) transmission.
 includes additional color sub sampling, improved
compression, error correction and multi-channel extensions for
surround sound.

32. MPEG- III
 MPEG-3 is the compression standard that never was.
 Originally evolved to support HD content.
 It turned out that MPEG- III could be done with minor changes to
MPEG-2. So MPEG-3 never happened.
 Now there are Profiles of MPEG-2 that support HDTV as well as
Standard Definition Television(SDTV).

33. MPEG- IV
 Goal: solve two video transport problems
• sending video over low-bandwidth channels
• achieving better compression than MPEG-2 for broadcast
signals.
 The MPEG committee designed MPEG-4 to be a single standard
covering the entire digital media workflow from capture, authoring
and editing to encoding, distribution, playback and archiving.
 Based on Apple Computer‟s QuickTime technology.
 Used in a wide range of bit rates, from 64 Kbits/s to 1,800
Mbits/s.

34. MPEG- IV Architecture

35. H.264/AVC
 H.264/MPEG4-AVC is a jointly developed standard by the ITU-T
Video Coding Experts Group (VCEG) and the ISO/IEC Moving
Picture Experts Group (MPEG) and has been standardized by the
ITU under the H.264 name.
 H.264 uses techniques fairly different from MPEG-2 and can match
the best MPEG-2 quality at up to half the data rate.
 Delivers excellent video quality across the entire bandwidth
spectrum from 3G to HDTV and everything in-between (from 40
Kbits/s to upwards of 10 Mbits/s).
 The H.264 design incorporates a Video Coding Layer (VCL), and
network Abstraction Layer(NAL)

36. JPEG
 JPEG stands for Joint photographic Experts Group.
 It exploits the fact that the human eye will not notice small color
changes in an image.
 Not a very good compression technique for full-color or grayscale
images.

37. AVI
 Stands for Audio Video Interleaved.
 Sound And Motion Picture File that conforms to the standards set
by Microsoft Windows Resource Interchange File Format (RIFF).
 The video quality is good at smaller resolutions.
 Only major drawback is that the files tend to be large.
 To play an .avi, you could use Windows Media
Player, RealPlayer, or the DivX player.

38. .MOV
 .mov is an Apple QuickTime motion video file format. Developed
by Apple Computer for viewing moving images.
 This file extension identifies an Apple QuickTime movie.
 .mov is a method of storing sound, graphics and movie files.

39. MJPEG
 Short for Motion JPEG.
 Best suited for broadcast resolution interlaced video, such as
NTSC or PAL.
 Each video field is separately compressed into a JPEG image.
 Also used for short files such as the short movies that can be
made by a digital camera.
 Not good for movies that are smaller than TV resolutions and ill
suited for progressive scan computer monitors.

40. DivX
 DivX is a software application that uses MPEG-4 standard to
compress digital video.
 DivX Networks and the open source community are developing
DivX jointly.

41. Scope of Development
 The MPEG committee continues to add video and graphics
standards, such as MPEG-7 and MPEG-21, to their standards
efforts.
 Digital video technology has become a necessity due to the
increasing demand to include video data for personal use as well
as in the entertainment industry, the corporate world, the
government and defense.
 Compression rates and the quality of data will continue to
improve, providing more efficient use of bandwidth, storage and
computing resources.

42. References and Further Reading
 www.wikipedia.com
 http://www.videomaker.com/article/10842/
 http://www.h264encoder.com/
 www.scribd.com
 http://ivythesis.typepad.com/term_paper_topics/2009/08/video-compression-
techniques.html
 http://drogo.cselt.stet.it/mpeg
 J.G. Apostolopoulos and S.J. Wee, ``Video Compression Standards'', Wiley Encyclopedia
of Electrical and Electronics Engineering, John Wiley & Sons, Inc., New York, 1999.
 V. Bhaskaranand K. Konstantinides, Image and Video Compression Standards:
Algorithms and Architectures, Boston, Massachusetts: KluwerAcademic
Publishers, 1997.
 J.L. Mitchell, W.B. Pennebaker, C.E. Fogg, and D.J. LeGall, MPEG Video Compression
Standard, New York: Chapman & Hall, 1997.