鉴于用神经网络实现图象压缩是一种非常有效的方法,为此提出了一种基于PCA/SOFM混合神经网络的图象压缩编码算法,并对SOFM网络学习参数的优化进行了探讨.实验证明,与PCA SOFM连续编码算法和基本SOFM算法相比,这种混合编码算法,由于占用存储空间少,因而降低了码书设计的计算量,并改善了码书的性能.

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; the substantial reduction of computation and the better performance of codebook.