Vector-Based Signal Processing and Quantization for Image and Video Compression

Image and video compression has become an increasingly important and active area. Many techniques have been developed in this area. Any compression technique can be modeled as a threestage process. The jirst stage can be generally called a signal processing stage where an image or video signal is converted into a direrent domain. Usually, there is no or little loss of information in this stage. The second stage is quantization where loss of infomtion occurs. The third stage is lossless coding that generates the compressed bit stream. The purpose of the signal processing stage is to convert an image or video signal into such a form that quantization can achieve better performance than without the signal processing stage. Because the quantization stage is the place where most of compression is achieved and loss of infomtion occurs, it is naturally the central stage of any compression technique. Since scalar quantization or vector quantization may be used in the second stage, the operation in the jirst stage should be scalar-based or vector-based respectively in order to match the second stage so that the compression performance can be optimized. In this paper, we summarize the most recent research results on vector-based signal processing and quantization techniques that have shown high compression performance.