Algorithm for Improved Image Compression and Reconstruction Performances

Energy efficient wavelet image transform algorithm (EEWITA) which is capable of evolving non-wavelet transforms consistently outperform wavelets when applied to a large class of images subject to quantization error. An EEWITA can evolve a set of coefficients which describes a matched forward and inverse transform pair that can be used at each level of a multi-resolution analysis (MRA) transform to minimize the original image size and the mean squared error (MSE) in the reconstructed image. Simulation results indicate that the benefit of using evolved transforms instead of wavelets increases in proportion to quantization level. Furthermore, coefficients evolved against a single representative training image generalize to effectively reduce MSE for a broad class of reconstructed images. In this paper an attempt has been made to perform the comparison of the performances of various wavelets and non-wavelets. Experimental results were obtained using different types of wavelets and non-wavelets for different types of photographic images (color and monochrome). These results concludes that the EEWITA method is competitive to well known methods for lossy image compression, in terms of compression ratio (CR), mean square error (MSE), peak signal to noise ratio (PSNR), encoding time, decoding time and transforming time or decomposition time. This analysis will help in choosing the wavelet for decomposition of images as required in a particular applications.