Fast-Searching Algorithm for Vector Quantization Using Projection and Triangular Inequality

In this paper, a new and fast-searching algorithm for vector quantization is presented. Two inequalities, one used for terminating the searching process and the other used to delete impossible codewords, are presented to reduce the distortion computations. Our algorithm makes use of a vector’s features (mean value, edge strength, and texture strength) to reject many unlikely codewords that cannot be rejected by other available approaches. Experimental results show that our algorithm is superior to other algorithms in terms of computing time and the number of distortion calculations. Compared with available approaches, our method can reduce the computing time and the number of distortion computations significantly. Compared with the best method of reducing distortion computation, our algorithm can further reduce the number of distortion calculations by 29% to 58.4%. Compared with the best encoding algorithm for vector quantization, our approach also further reduces the computing time by 8% to 47.7%.