Based on the property analysis of interferential multispectral images, a novel compression algorithm of partial set partitioning in hierarchical trees （SPIHT） with classified weighted rate-distortion optimization is presented. After wavelet decomposition, partial SPIHT is applied to each zero tree independently by adaptively selecting one of three coding modes according to the probability of the significant coefficients in each bitplane. Meanwhile the interferential multispectral image is partitioned into two kinds of regions in terms of luminous intensity, and the rate-distortion slopes of zero trees are then lifted with classified weights according to their distortion contribution to the constructed spectrum. Finally a global rate- distortion optimization truncation is performed. Compared with the conventional methods, the proposed algorithm not only improves the performance in spatial domain but also reduces the distortion in spectral domain.
This paper proposes a method of error detection based on macroblock （MB） types for video transmission. For decoded inter MBs, the absolute values of received residues are accumulated. At the same time, the intra textural complexity of the current MB is estimated by that of the motion compensated reference block. We compare the inter residue with the intra textural complexity. If the inter residue is larger than the intra textural complexity by a predefined threshold, the MB is considered to be erroneous and errors are concealed. For decoded intra MBs, the connective smoothness of the current MB with neighboring MBs is tested to find erroneous MBs. Simulation results show that the new method can remove those seriously-corrupted MBs efficiently. Combined with error concealment, the new method improves the recovered quality at the decoder by about 0.5--1 dB.
A nonlinear data analysis algorithm, namely empirical data decomposition （EDD） is proposed, which can perform adaptive analysis of observed data. Analysis filter, which is not a linear constant coefficient filter, is automatically determined by observed data, and is able to implement multi-resolution analysis as wavelet transform. The algorithm is suitable for analyzing non-stationary data and can effectively wipe off the relevance of observed data. Then through discussing the applications of EDD in image compression, the paper presents a 2-dimension data decomposition framework and makes some modifications of contexts used by Embedded Block Coding with Optimized Truncation （EBCOT） . Simulation results show that EDD is more suitable for non-stationary image data compression.
