Performance Measure of Different Wavelets for a Shuffled Image Compression Scheme

In the modern world of technologies, the main constraint of limitation is the memory of the system. Memory plays a key role in the multimedia devices and the data storage devices, where the images are considerably bulky. To compress the image, the previously used technologies include Discrete Cosine Transform wherein there are more Blocking Artifacts and floor operator loss due to which the quality of reconstructed image is degraded and utilizes more Bandwidth. The paper discusses the important features of wavelet transform in compression of still images, including the extent to which the image quality is degraded by compression and decompression process. In this paper, the optimum method of wavelet transformation is explored. Performance Measure of different Wavelets is compared with and without shuffling scheme. By using these wavelets and compression, we can achieve an optimum balance between the performance metrics like Peak Signal to Noise Ratio and Compression Ratio and also reduces the Mean Square Error. Our results provide a good reference for application developers to choose a good wavelet compression system for their application. 1