Multiwavlet-based Image Vector Quantization

Vision is the most important way for one to get information from the outer world ."It is better to see for oneself rather than to hear for many times" , and one can get intuitionistic, visualized impression by vision .With the fast development of computer and digital communication technology , especially with the rise of Internet and multimedia, the image compression has attracted more and more attention.Wavelet analysis is a new kind of time-frequency technology .which developed on the basis of Fourier Transform, and it has good space-frequency localization characteristic.As the further development of wavelet multiwavelet not only keeps the excellent characteristics of time and frequency field owned by wavelet but also overcomes the defects of wavelet. Multiwavelet combines smoothness, compactness, symmetry and orthogonality which are very important in practice ,and especially in image compression it has better functions than wavlet, which results in more and more research and applications on it.As one method of image lossy compression vector quantization has great compression ratio. Generally speaking, compression ratio is bigger and hence distortion is bigger, on the other hand, compression ratio is smaller and hence distortion is smaller. At the same time vector quantization is also a process with a lot of computing, but with some ameliorative algorithms , vector quantization has become more and more maturational .The central work of this paper is to probe into the coefficients which are made from a image by multiwavlet, and according to their distribution characteristic a vector quantization algorithm is put forward. In experiment based on CL multiwavelet and SOFM a image vectorquantization design is advanced. There are two creations, as follows, First, combine multiwavlet and SOFM to make a vector quantization. Second, divide coefficients into three parts according to the characteristic of them. Then resolve the problem of shortage of memory, and at the same time improve the speed of coding and en-coding.Of course, it is very important for a self organization feature map network to select a good initial training collection, and image Lena is adopted to do the vital job, image Lena has a rich frequency spectrum, which can avoid the local optimization of code table, and partition the frequency space equably to make the code table fit for as many image as possible .The experiment result show that the code table which is made by image Lena can fit for many different statistic feature images to make vector quantizations.In the end, we make a conclusion and look forward to the future of multiwavlet with vector quantization , and put forward some possible technology.