An efficient VQ algorithm using mean value predictive

Vector Quantization (VQ) [1], is an efficient technique for signal compression. In traditional VQ, the major computation is on searching the nearest codeword of the codebook for every input vector. Our search algorithm is based on partial distance Elimination (PDE) [2] and binary search is used to determine first search point. We sort the codebook by the mean value in pre-processing before all the practical compression. The first search point is the closest mean value between the input vector and the codewords in the codebook. Then, find the best match codeword by PDE to reduce the search time. Moreover, we also need another table to record the Euclidean distance [3]. In this Euclidean mapping table, we will calculate results of Euclidean distance from 0 to 255. Generating the table also does not increase any encoding time, because it is yielded off-line. In order to find the first search point, we calculate the mean value of input vector and use the binary search to decide the first search point in the codebook. We will set a static search range for every input vector and calculate the distortion between the input vector and each codeword. Then select the minimum distortion to be the index. The proposed algorithm demonstrates outstanding performance in terms of the time saving and arithmetic operations. Compared to full search algorithm, it saves more than 95 % search time. Test image Lena with the 8-bit gray and 512 by 512 resolutions is within the training image to get the codebook. Codebook size is 256 and each codeword is 4x4 dimensions. Table 1 lists the simulation comparisons with full search