Image Compression with Different Types of Wavelets

Data compression which can be lossy or lossless is required to decrease the storage requirement and better data transfer rate. One of the best image compression techniques is using wavelet transform. It is comparatively new and has many advantages over others. Wavelet transform uses a large variety of wavelets for decomposition of images. The state of the art coding techniques like EZW, SPIHT (set partitioning in hierarchical trees) and EBCOT (embedded block coding with optimized truncation) use the wavelet transform as basic and common step for their own further technical advantages. The wavelet transform results therefore have the importance which is dependent on the type of wavelet used. In this paper, different wavelets have been used to perform the transform of a test image and their results have been discussed and analyzed. The analysis has been carried out in terms of PSNR (peak signal to noise ratio) obtained and time taken for decomposition and reconstruction. SPIHT coding algorithm is considered as a basic standard in compression field using wavelet transform. In addition to wavelet analysis for simple decomposition, analysis of SPIHT coding algorithm in terms of PSNR for different wavelets is also carried out here. This analysis will help in choosing the wavelet for decomposition of images as per their application