Memory efficient and high-speed search Huffman coding

Code compression is a key element in high-speed digital data transport. A major compression is performed by converting the fixed-length codes to variable-length codes through a (semi-)entropy coding scheme. Huffman coding is shown to be a very efficient coding scheme. To speed up the process of search for a symbol in a Huffman tree and to reduce the memory size we have proposed a tree clustering algorithm to avoid high sparsity of the tree. The method is shown to be extremely efficient in memory requirement, and fast in searching for the symbol. For an experimental video data with Huffman codes extended up to 13 bits in length, the entire memory space is shown to be 122 words, compared to 2¹³=8192 words in a normal situation