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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).

This paper discusses about embedded zerotree wavelet (EZW) and other wavelet-based encoding techniques employed in lossy image compression. The objective of this paper is two fold. Primarily wavelet-based encoding techniques such as EZW, set partitioning in hierarchical trees (SPIHT), wavelet difference reduction (WDR), adaptively scanned wavelet difference reduction (ASWDR), set partitioned embedded block (SPECK), compression with reversible embedded wavelet (CREW) and space frequency quantization (SFQ) are implemented and their performance is analyzed. Second, wavelet-based compression schemes such as Haar, Daubechies and Biorthogonal are used to evaluate the performance of encoding techniques. The performance parameters such as peak signal-to-noise ratio (PSNR) and mean square error (MSE) are used for evaluation purpose. From the results it is observed that the performance of SPIHT encoding technique is providing better results when compared to other encoding schemes.