Narrow Results

When the embedded zerotree wavelet (EZW) algorithm was first introduced by Shapiro, four types of symbols (zerotree (ZTR), isolated zero (IZ), positive (POS), and negative (NEG)) were used to represent the tree structure. An improved version of EZW, the set partitioning in hierarchical trees (SPIHT) algorithm was later proposed by Said and Pearlman. SPIHT removed the ZTR symbol, while keeping the other three symbols in a slightly different form. In the SPIHT algorithm, the coding of the parent node is isolated from the coding of its descendants in the tree structure. Therefore, it is no longer possible to encode the parent and its descendants with a single symbol. When both the parent and its descendants are insignificant (forming a degree-0 zerotree (ZTR)), it cannot be represented using a ZTR symbol. From our observation, the number of degree-0 ZTRs can occur very frequently not only in natural and synthesis images, but also in video sequences. Hence, the ZTR symbol is reintroduced into SPIHT in our proposed SPIHT-ZTR algorithm. In order to achieve this, the order of sending the output bits was modified to accommodate the use of ZTR symbol. Moreover, the significant offspring were also encoded using a slightly different method to further enhance the performance. The SPIHT-ZTR algorithm was evaluated on images and video sequences. From the simulation results, the performance of binary-uncoded SPIHT-ZTR is higher than binary-uncoded SPIHT and close to SPIHT with adaptive arithmetic coding.

This paper presents a complete analysis of wavelet-based image compression encoding techniques. The techniques involved in this paper are embedded zerotree wavelet (EZW), set partitioning in hierarchical trees (SPIHT), wavelet difference reduction (WDR), adaptively scanned wavelet difference reduction (ASWDR), set partitioned embedded block coder (SPECK), compression with reversible embedded wavelet (CREW) and spatial orientation tree wavelet (STW). Experiments are done by varying level of the decomposition, bits per pixel and compression ratio. The evaluation is done by taking parameters like peak signal to noise ratio (PSNR), mean square error (MSE), image quality index (IQI) and structural similarity index (SSIM), average difference (AD), normalized cross-correlation (NK), structural content (SC), maximum difference (MD), Laplacian mean squared error (LMSE) and normalized absolute error (NAE).