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Source Coding. Jean Walrand EECS. Outline. Compression Losless: Huffman Lempel-Ziv Audio: Examples Differential ADPCM SUBBAND CELP Video: Discrete Cosine Transform Motion Compensation. Compression. Goal:. Reduce the number of bits to encode source. Approaches:.

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source coding

Source Coding

Jean WalrandEECS

outline
Outline
  • Compression
  • Losless:
    • Huffman
    • Lempel-Ziv
  • Audio:
    • Examples
    • Differential
    • ADPCM
    • SUBBAND
    • CELP
  • Video:
    • Discrete Cosine Transform
    • Motion Compensation
compression
Compression
  • Goal:
  • Reduce the number of bits to encode source
  • Approaches:
  • Lossless: For data
  • Lossy: For voice, video
huffman encoding
Lossless

Key Idea: Use shorter code words for more frequent symbols

EX1:

Huffman Encoding
huffman encoding continued1
If the symbols are independent and identically distributed, the Huffman encoding is the prefix-free code with the minimum average number of bits.

Note: The Shannon encoding requires fewer bits, but requires encoding large blocks of symbols.

Both codes assume that the distribution is known.

Huffman Encoding(continued)
lempel ziv
Lempel-Ziv
  • Lossless
  • Symbols are not independent
  • Distribution is not known
  • Want to minimize the average number of bits
  • Typical application: any file
  • Approach: Build dictionary and replace string with location of prefix in the dictionary
audio
Audio
  • Examples:
    • Speech:
      • PCM 64kbps
      • ADPCM 32-64kbps
      • SBC 16-32kbps
      • VSELP-CELP 2.4-8kbps
    • Audio:
      • PCM 1400kbps
      • MPEG 48-384kbps
audio c d
Differential Encoding (also used for Video):

Key Idea is that differences between successive samples may be small

Difficulty: Error Propagation

Audio (c’d)
video
Discrete Cosine Transform

Objective: Extract “Visible Information”

Video

f(x, y) = Sm,n F(m, n) cos(mx) cos(ny)

video cd
Motion Compensation

Idea: Track motion of picture

Encode (motion vector, modification)

Video (cd)