A fast vector quantization image encoding using Tchebichef moments

Abstract

The codeword searching sequence is sometimes vital to the efficiency of a VQ encoding algorithm. In this paper, we present a fast encoding algorithm for vector quantization that uses Tchebichef moments of an image block three characteristics of a vector: linear projection, variance and third moment. A similar method using linear projection and variance of an image block was already proposed (EENNS, IEENNS). Severeal new inequalities based on Tchebichef moments of a image block are introduced to reject those codewords that are impossible to be the nearest codevector and cannot be rejected by inequalities based on sum and variance, thereby saving a great deal of computational time, while introducing no extra distortion compared to the conventional full search algorithm. The simulation results confirm the effectiveness of the proposed algorithm compared with improved equal-average equal-variance nearest neighbor search (IEENNS).