NEURAL NETWORKS FOR CLASSIFIED VECTOR QUANTIZATION OF IMAGES

Recently, the vector quantization (VQ) technique has received considerable attention and has become an effective tool for data compression. It provides high compression ratios and simple decoding processes for digital images. However, studies of practical implementation of VQ have revealed some major difficulties such as edge integrity of the reconstructed images and computational complexity of codebook design. Over the past few years, a new wave of research in neural networks has emerged. Neural network models have provided an effective means of solving computationally intensive problems. This paper proposes the implementation of classified vector quantization for image compression with neural network models. In order to preserve the edge integrity and improve the efficiency of codebook design, the proposed method includes a multilayer perceptron model for edge classification and a self-organization model for codebook design. A system architecture is proposed, and simulation results demonstrate improvements in visual quality and input encoding.