Conditional entropy-constrained residual VQ with application to image coding

作     者：Kossentini, F Chung, WC Smith, MJT 

作者机构：Digital Signal Processing Laboratory School of Electrical and Computer Engineering Georgia Institute of Technology Atlanta GA USA 

出 版 物：《IEEE TRANSACTIONS ON IMAGE PROCESSING》 (IEEE Trans Image Process)

年 卷 期：1996年第5卷第2期

页      面：311-320页

核心收录：

学科分类：0808[工学-电气工程] 08[工学] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：National Science Foundation, NSF, (MIP-9116113) National Aeronautics and Space Administration, NASA 

主　　题：image coding complexity classes progressive transmission entropy Vector quantization RESIDUAL memory requirements Conditional 

摘      要：This paper introduces an extension of entropy-constrained residual vector quantization (VQ) where intervector dependencies are exploited, The method, which we call conditional entropy-constrained residual VQ, employs a high-order entropy conditioning strategy that captures local information in the neighboring vectors, When applied to coding images, the proposed method is shown to achieve better rate-distortion performance than that of entropy-constrained residual vector quantization with less computational complexity and lower memory requirements. Moreover, it can be designed to support progressive transmission in a natural way, It is also shown to outperform some of the best predictive and finite-state VQ techniques reported in the literature, This is due partly to the joint optimization between the residual vector quantizer and a high-order conditional entropy coder as well as the efficiency of the multistage residual VQ structure and the dynamic nature of the prediction.