An Improved Fast Encoding Algorithm for Vector Quantization

In the current information age, people have to access various information. With the popularization of the Internet in all kinds of information fields and the development of communication technology, more and more information has to be processed in high speed. Data compression is one of the techniques in information data processing applications and spreading images. The objective of data compression is to reduce data rate for transmission and storage. Vector quantization (VQ) is a very powerful method for data compression. One of the key problems for the basic VQ method, i.e., full search algorithm, is that it is computationally intensive and is difficult for real time processing. Many fast encoding algorithms have been developed for this reason. In this paper, we present a reasonable half-L2-norm pyramid data structure and a new method of searching and processing codewords to significantly speed up the searching process especially for high dimensional vectors and codebook with large size; reduce the actual requirement for memory, which is preferred in hardware implementation system, e.g., SOC (system-on-chip); and produce the same encoded image quality as full search algorithm. Simulation results show that the proposed method outperforms some existing related fast encoding algorithms.