Research On Image Coding Algorithms Based On Finite State Vector Quantization

Digital technique is one of the most powerful impetuses in the development of information society. Especially in recent years, with the proliferation of Internet, mobile telephone and digital TV, the demands of digital information become urgent and abundant. Since digital information needs a large amount of bits to express, storage and transmission have become one of the bottlenecks that obstruct people to obtain and make use of it. Image information takes eighty percent of all types of digital information. Therefore, image compression coding technique becomes one of the most important directions in the filed of communication and signal processing. The technique of Finite-state vector quantization (FSVQ) has a potential capability of improving coding performance. However, the development of the Image Coding FSVQ algorithm has not been perfect. So in this paper, an in-depth study of Static Image FSVQ coding algorithm is conducted as well as two kinds of improving algorithms is introduced. The major jobs are described next:1. Basic knowledge of image and main methods of image compression are introduced. At first, the development of image compression coding and FSVQ coding is reviewed simply, then the estimate methods of image quality is discussed, providing a preparation for image compression coding.2. Relying on the study of the FSVQ algorithm and its state transfer function, a Refined Gradient Classifier is pointed out to promote the forecast preciseness of input vector. Considering that the size of super codebooks and state codebooks are generally small, a dynamic FSVQ coding algorithm based on gradient is given to enhance the effect of compression and guarantee the image quality.3. Combined with FSVQ algorithm, the traditional DCT transform and integer DCT transform are used to improve the performance of image compression.4. Depending on the analysis of principle that wavelet-base selected by static image compression coding, the most suitable wavelet for FSVQ coding is selected after comparing groups of the waveler-base. A mixed coding algorithm that adopts Distortion Constrained Codebook Replenishment is presented based on the S+P transform dynamic FSVQ coding algorithm.5. Considering the fact that human visual system is the final arbiter of image quality, this paper conducts a research of human visual characteristics. During the process of encoding, a method of Weighted Mean Squared Error (WMSE) based on human visual system is produced, which obtains a good vision effect of the coding image.6. The major contribution of our research work is that a Static Image Compression software platform has been constructed. The traditional Image Compression algorithms and the improved algorithms in this paper have been compared and emulated on this platform.