Entropy coding Present by 陳群元

outline

constraints  Compression efficiency  Computational efficiency  Error robustness

Encoding  DCT  Reordering  Run-level coding  Entropy coding

Encoding  DCT  Reordering  Run-level coding  Entropy coding

DCT

Reordering  The optimum method of reordering the quantised data depends on the distribution of the non-zero coefficients.

Evenly distribution

Zigzag reordering pattern

Interlaced video-vary more in vertical

Modified reordering pattern

Encoding  DCT  Reordering  Run-level coding  Entropy coding

Run-level coding  Long sequences of identical values (zeros in this case) can be represented as a (run, level) code  (run) indicates the number of zeros preceding a non-zero value  (level) indicates the sign and magnitude of the non-zero coefficient

Run-level coding(ex)

Encoding  DCT  Reordering  Run-level coding  Entropy coding

Huffman Coding  ‘True’ Huffman Coding  Modified Huffman Coding  Table Design  Entropy Coding Example  Variable Length Encoder Design  Variable Length Decoder Design  Dealing with Errors

Huffman Coding  ‘True’ Huffman Coding  Modified Huffman Coding  Table Design  Entropy Coding Example  Variable Length Encoder Design  Variable Length Decoder Design  Dealing with Errors

True Huffman Coding

Generating the HufSman code tree  1. Order the list of data in increasing order of probability.  2. Combine the two lowest-probability data items into a ‘node’ and assign the joint probability of the data items to this node.  3. Reorder the remaining data items and node(s) in increasing order of probability and repeat step 2.

encoding

decoding

Carphone vs claire

claire

disadvantage  the decoder must use the same codeword set as the encoder  reduces compression efficiency.  calculating the probability table for a large video sequence a significant computational overhead  cannot be done until after the video data is encoded

Huffman Coding  ‘True’ Huffman Coding  Modified Huffman Coding  Table Design  Entropy Coding Example  Variable Length Encoder Design  Variable Length Decoder Design  Dealing with Errors

Table design Final pair

Table design

H.263/MPEG-4 motion vector difference (MVD)  The H.263MPEG-4 differentially coded motion vectors (MVD)  are each encoded as a pair of VLCs, one for the x- component and one for the y-component

mvd

H.26L universal VLC (UVLC)  The emerging H.26L standard takes a step away from individually calculated Huffman tables by using a ‘universal’ set of VLCs for any coded element. Each codeword is generated from the following systematic list:

Huffman Coding  ‘True’ Huffman Coding  Modified Huffman Coding  Table Design  Entropy Coding Example  Variable Length Encoder Design  Variable Length Decoder Design  Dealing with Errors

Entropy Coding Example

Huffman Coding  ‘True’ Huffman Coding  Modified Huffman Coding  Table Design  Entropy Coding Example  Variable Length Encoder Design  Variable Length Decoder Design  Dealing with Errors

Variable Length Encoder Design

Huffman Coding  ‘True’ Huffman Coding  Modified Huffman Coding  Table Design  Entropy Coding Example  Variable Length Encoder Design  Variable Length Decoder Design  Dealing with Errors

Variable Length Decoder Design

Huffman Coding  ‘True’ Huffman Coding  Modified Huffman Coding  Table Design  Entropy Coding Example  Variable Length Encoder Design  Variable Length Decoder Design  Dealing with Errors

Dealing with Errors

Arithmetic Coding

Ex. encode (0,-1,0,2) 0.394

Ex. decode (0,-1,0,2)

Arithmetic coding vs huffman  Ideal  in this case, which can be represented as a fixed- point number with sufficient accuracy using 9 bits  Huffman: 1;0011;1; =>13 bits

 The end  Thank you