A fast nearest neighbor search algorithm for image vector quantization

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 three characteristics of a vector: linear projection, variance and third moment. A similar method using the first two features was already proposed. A third inequality, based on third geometric-moment of a vector, is 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).