Improved image decoding over noisy channels using minimum mean-squared estimation and a Markov mesh

作     者：Park, MS Miller, DJ 

作者机构：Penn State Univ Dept Elect Engn University Pk PA 16802 USA 

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

年 卷 期：1999年第8卷第6期

页      面：863-867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  (IIS-9624870) 

主　　题：hidden Markov models image coding joint source-channel coding least mean square methods Markov mesh model 

摘      要：Joint source-channel (JSC) decoding based on residual source redundancy is a technique for providing channel robustness to quantized data. Previous work assumed a model equivalent to viewing the encoder/noisy channel tandem as a discrete hidden Markov model (HMM) with transmitted indices the hidden, states. Here, me generalize this HMM-based (I-D) approach for images, using the more powerful hidden Markov mesh random field (HMMRF) model. While previous state estimation methods for HMMRF s base estimates on only a causal subset of the observed data, our new method uses both causal and anticausal subsets. For JSC-based image decoding, the new method provides significant benefits over several competing techniques.