Codebook Design Based On SOFM Neural Network And Its Application On Image Transmission

With the rapid development of the computer network and communication technology, analyzing and processing digital signal have been greatly developed, in the areas of communications, radar and automation. The image plays an important role on expression of information, therefore, in order to improve the efficiency of transmission and reduce the space of storage, we must adopt effective coding algorithms to eliminate redundant information in the image, and use the number of bits as little as possible to describe the image under the condition of given distortions. As an effective lossy compression techniques, vector quantization has the highlighting advantages of larger compression ratio and easy decoding algorithm, therefore, it has become an important image compression coding technology.Classic vector quantization codebook-design algorithm - LBG algorithm, it adapts dynamic clustering idea at the basis of given initial codebook, in each iteration, it uses the conditions of nearest neighbor(the optimal division) and the center of mass (the optimal codebook) in turn until convergence, then put the convergent codebook as the final codebook. However, it has the following drawbacks: 1)it is very sensitive to initial codebook; 2) the algorithm is a batch algorithm, every iteration has to be dealed with all training data, so it's lack of flexibility and adaptability. In order to overcome these drawbacks, people have done many researches on codebook's design. In recent years, neural network has been successfully applied to the vector quantization codebook's design.The article introduces codebook-design algorithm about the learning vector quantization , focus on self-organizing feature map neural network for codebook-design algorithm, and proposes an improved algorithm for its drawbacks. The simulation's tests prove that the performance of the improved codebook-design algorithm is better in the reconstructed image's signal to noise ratio and peak signal to noise ratio.To save image's transmission, processing time and reduce storage's capacity, we use image compression coding techniques to reduce the data describing the image (ie, bits), when we code digital image, it will be involve a large amount of calculation, if it directly treated in the space domain. So we often use a variety of image-transform methods such as Fourier transform, Walsh transform, wavelet transform, discrete cosine transform and so on, which will convert the spatial domain to the transform domain. It will not only can reduce the computation, but also could receive more effective treatment.(such as Fourier transform handles digital filter in frequency domain).In this paper, the basic principles of discrete cosine transform, discrete walsh-hadamard transform and wavelet transform are introduced, at the basis of these studies, we will apply codebook obtained by improved algorithm to the image compression encoding method of VQ + DPCM + DCT . Finally, we list the simulation's results.