A fast search algorithm for mean-removed vector quantization using edge and texture strengths of a vector

In this paper, a fast search algorithm for mean-removed vector quantization is proposed. Two inequalities are used to reduce distortion computations. Our algorithm makes use of a mean-removed vector's features (edge and texture strengths) to reject many unlikely codewords and it has the same image quality as the full search method. Experimental results show that our algorithm is much better than the full search method in terms of computing time and the number of distortion calculations. Comparing with the full search method, our method can effectively reduce the computing time by 60.2-94.2% and the number of distortion computations by 78.6-97.9% for the codebook sizes of 64-2048. As far as we know, our method is the first of its kind to reduce the encoding time for mean-removed vector quantization.