Previous research advances have shown that wavelet-based image-compression techniques offer several advantages over traditional techniques in terms of progressive transmission capability, compression efficiency, and bandwidth utilization. The embedded zerotree wavelet (EZW) coding technique suggested by Shapiro (1992), and its modification-set partitioning in hierarchical trees (SPIHT), suggested by Said and Pearlman (19996)-demonstrate the competitive performance of wavelet-based compression schemes. The EZW-based lossless image coding framework consists of three stages: (1) reversible discrete wavelet transform; (2) hierarchical ordering and selection of wavelet coefficients; and (3) context-modeling-based entropy (arithmetic) coding. The performance of the compression algorithm depends on the choice of various parameters and the implementation strategies employed in all the three stages. This paper proposes different context modeling and selection techniques for efficient entropy encoding of wavelet coefficients, along with the modifications performed to the SPIHT algorithm. The results of several experiments presented in this paper demonstrate the importance of context modeling in the EZW framework. Furthermore, this paper shows that appropriate context modeling improves the performance of compression algorithm after a multilevel subband decomposition is performed.
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