An image compressing algorithm based on PCA/SOFM hybrid neural network

Neural network is a very efficient method for image compression. It is suited to the problem of image compression due to its massively parallel and distributed architecture. Principle component analysis (PCA) neural network model and self-organizing feature map (SOFM) neural network model are often adopted for image compression in many references. In this paper, the authors propose an image compressing algorithm based on PCA/SOFM hybrid neural network, which has the advantages of both PCA and SOFM. A new method of selecting initial codebook and distortion criterion is presented to improve the efficiency of SOFM neural network according to the statistical feature of PCA transformational coefficient. Simulation results show that compared to successive PCA and SOFM algorithm or basic SOFM algorithm, PCA/SOFM hybrid algorithm has many advantages: lower memory storage; substantial reduction of computation and better performance of codebook.