Huffman coding of DCT coefficients using dynamic codeword assignment and adaptive codebook selection

In many image sequence compression applications, Huffman coding is used to eliminate statistical redundancy resident in given data. The Huffman table is often pre-defined to reduce coding delay and table transmission overhead. Local symbol statistics, however, may be much different from the global ones manifested in the pre-defined table. In this paper, we propose three Huffman coding methods in which pre-defined codebooks are effectively manipulated according to local symbol statistics. The first proposed method dynamically modifies the symbol-codeword association without rebuilding the Huffman tree itself. The encoder and decoder maintain identical symbol-codeword association by performing the same modifications to the Huffman table, thus eliminating extra transmission overhead. The second method adaptively selects a codebook from a set of given ones, which produces the minimum number of bits. The transmission overhead in this method is the codebook selection information, which is observed to be negligible compared with the bit saving attained. Finally, we combine the two aforementioned methods to further improve compression efficiency. Experiments are carried out using five test image sequences to demonstrate the compression performance of the proposed methods.