UCBSource Coding

Jean WalrandEECS

UCB Outline Compression Losless:

Huffman Lempel-Ziv

Audio: Examples Differential ADPCM SUBBAND CELP

Video: Discrete Cosine Transform Motion Compensation

UCB Compression

Goal: Reduce the number of bits to encode source

Lossless: For data Lossy: For voice, video

Approaches:

UCB Huffman Encoding

Lossless Key Idea: Use shorter code words for

more frequent symbols EX1:

UCB Huffman Encoding(continued)

EX2:

UCB Huffman Encoding(continued)

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.

UCB 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

UCB Lempel-Ziv(continued)

Example:

UCB Audio Examples:

Speech: PCM 64kbps ADPCM 32-64kbps SBC 16-32kbps VSELP-CELP 2.4-8kbps

Audio: PCM 1400kbps MPEG 48-384kbps

UCB Audio (c’d)

Differential Encoding (also used for Video): Key Idea is that differences between

successive samples may be small Difficulty: Error Propagation

UCB Audio (c’d)

Differential Encoding (c’d)

UCB Audio (c’d)

ADPCM: Adaptive Differential PCMPredict next value, encode error

UCB Audio (c’d)

Sub-Band Coding: Improves performance

UCB Audio (c’d)

CELP (Code Excited Linear Predictor)

UCB Video

Discrete Cosine Transform Objective: Extract “Visible Information”

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

UCB Video (cd)

Motion Compensation Idea: Track motion of picture Encode (motion vector, modification)