VECTOR-BASED SIGNAL-PROCESSING AND QUANTIZATION FOR IMAGE AND VIDEO COMPRESSION

Image and video compression has become an increasingly important and a ctive area. Many techniques have been developed in this area. Any comp ression technique can be modeled as a three-stage process. The first s tage can be generally called a signal processing stage where an image or video signal is converted into a different domain. Usually, there i s no or little loss of information in this stage. The second stage is quantization where loss of information occurs. The third stage is loss less coding that generates the compressed bit stream. The purpose of t he signal processing stage is to convert an image or video signal into such a form that quantization can achieve better performance than wit hout the signal processing stage. Because the quantization stage is th e place where most of compression is achieved and loss of information 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 first stage should be scalar-based or vector-based respectively in order to match the second stage so tha t the compression performance can be optimized. In this paper, we summ arize the most recent research results on vector-based signal processi ng and quantization techniques that have shown high compression perfor mance.