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Multimedia Compression

Multimedia Compression. B90901134 陳威尹. Why Compress. Raw data are huge. Audio: CD quality music 44.1kHz*16bit*2 channel=1.4Mbps Video: near-DVD quality true color animation 640px*480px*30fps*24bit=220Mbps Impractical in storage and bandwidth. Outline. Generic Compression Overview

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Multimedia Compression

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  1. Multimedia Compression B90901134 陳威尹

  2. Why Compress • Raw data are huge. • Audio:CD quality music44.1kHz*16bit*2 channel=1.4Mbps • Video:near-DVD quality true color animation640px*480px*30fps*24bit=220Mbps • Impractical in storage and bandwidth

  3. Outline • Generic Compression Overview • Content specific Compression • Lossy Compression

  4. Introduction to Generic Compression Algorithm Lossless Compression

  5. Generic Compression • Also called Entropy Encoding • Lossless Compression Algorithms • Entropy can defined as: • Need statistical knowledge of data • Well-known Algorithms: • Rice coding • Huffman coding • Arithmetic coding

  6. Huffman encoding Input: ABACDEAACCAABEAABACBDDABCADDBCEAEAAADBE Order-0 model Symbol A B C D E Count 15 7 6 6 5 total:39*3=117 bits Output: 15*1+(7+6+6+5)*3=87 bits Compression ratio: 117/87 = 1.34

  7. Property of Huffman encoding • Easy to implement, high encoding speed • Unique Prefix Property: no code is a prefix to any other code • Adaptive Huffman encoding: • statistical knowledge not available • update Huffman tree when needed

  8. Arithmetic Encoding • Symbol X, Yprob(X) = 2/3prob(Y) = 1/3

  9. Property of Arithmetic Encoding • Prevent entropy wasting in Huffman coding, for the number of bits to represent a symbol can be non-integer • About 5~10% smaller than Huffman coding • Computational intensive • US patented!! • Both Huffman and Arithmetic are used in the entropy encoding stage in JPEG

  10. Application of General Compression • Generic file compression like Zip, Rar, gzip, bzip, etc. • Final stage of content specific compression • JPEG uses Huffman or Arithmetic • Monkey’s Audio (ape) uses Rice • Lossless Audio (La) uses Arithmetic

  11. Content specific Compression Further De-correlation

  12. De-correlation • Correlation means redundancy • However, general algorithm may not find content-specific correlation • General algorithm of higher order may not be efficient enough • No matter lossy or lossless, multimedia file format use content-specific pre-filter as 1st step to reduce data redundancy.

  13. Correlation in Multimedia • Audio: • Temporal, Channel • Still Image: • Color space, Spatial, Stereo • Video: • Temporal

  14. Audio Channel Correlation • Correlation between L/R channels • L/R to mid/pass band conversion • More complex decorrelation in more channels

  15. Color Space Correlation • Correlation between color channels • map RGB to YUV color space Y = 0.299*R + 0.587*G + 0.114*B U = -0.169*R - 0.331*G + 0.500*B + 128.0 V = 0.500*R - 0.419*G - 0.081*B + 128.0 • Example in PNG

  16. R 95KB G 96KB B 98KB Y 97KB U 32KB V 37KB Color Space Correlation-- RGB to YUV Conversion

  17. Video Channel Correlation • Multi-view channel in 3D video • convert to Image and Depth channel • Disparity Estimation (like Motion Estimation)

  18. Search Range Motion Vector Reference Frame Current Frame Video Temporal Correlation • Similarity between adjacent frames • Motion estimation and motion compensation (mostly Lossy)

  19. Lossless is not enough! • The best lossless audio and image compression ratio is normally a half • Lossy audio compression like mp3 or ogg achieve 1/20 ratio while remain acceptable quality, and 1/5 ratio for impeccable quality • Lossy video compression reduce a film to 1/300 size

  20. Lossy Compression Loss of data lead to higher compression ratio

  21. Lossy Compression • Massively reduce information we don’t notice • Highly content specific • Psychology

  22. Lossy Audio Compression • Frequency domain • Quantization • The importance varies in bands • Higher frequency, larger quantum • Psychoacoustics • Pitch resolution of ear is only 2Hz without beating • Threshold of hearing varies in bands • Simultaneous and temporal masking effect

  23. Transform Quantization Entropy Coding Image data Output data Lossy Image Compression • Frequency domain • Discrete Cosine Transform (in Jpeg) • Discrete Wavelet Transform (in J2k) • Quantization • Reduce less important data

  24. DCT Discrete Cosine Transform 8x8 Quantization Table Huffman Coding JPEG Transform Quantization Entropy Coding DWT Discrete Wavelet Transform Quantization for each sub-band Arithmetic Coding J2K Jpeg2000 vs. Jpeg

  25. Lossy Image Compression in Practice (1) • Original

  26. Lossy Image Compression in Practice (2) • Transform domain coefficients. • Only a few components are visible for each 8x8 block. • The DC component is in the upper left of each block

  27. Lossy Image Compression in Practice (3) • After quantization and IDCT. • Note clearly seen blocky effect. • Compression ratio = 17.8:1 with an SNR of 20.1 dB, not including entropy encoding

  28. Lossy Video Compression

  29. Motion Estimation

  30. Motion Compensation Without motion compensation With motion compensation

  31. Frame Type • Intra Frame (I) • Predictive Frame (P) • Bidirectional predictive Frame (B)

  32. Video Compression Demo • Motion Vector and bandwidth overlaid on mpeg4 video using ffdshow-20041012

  33. Reference • Lossless Compression Algorithmshttp://www.cs.cf.ac.uk/Dave/Multimedia/node207.html • Monkey’s Audiohttp://www.monkeysaudio.com/theory.html • Lossless Audio (La)http://www.lossless-audio.com/theory.htm • Compression and speed of lossless audio formatshttp://web.inter.nl.net/users/hvdh/lossless/main.htmhttp://members.home.nl/w.speek/comparison.htm • http://www.wordiq.com/definition/Wavelet_compression • http://www.wordiq.com/definition/Psychoacoustics • http://www.wordiq.com/definition/MP3 • H.264http://www.komatsu-trilink.jp/device/pdf11/UBV2003.pdf

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