Implementation of Image Compression using fast Wavelet Transform using HAAR and Daubechies Wavelets

Image compression is the application of Data compression on digital images. The objective of image compression is to reduce redundancy of the image data in order to be able to store or transmit data in an efficient form. Fast Wavelet Transform (FWT) highlights the benefit of a faster compression and faster processing as compared to DWT with higher compression ratios at the same time and reasonably good image quality. Fast Wavelet Transform has been used to perform image compression. Haar and Daubechies wavelets have been implemented and comparitive results of fast wavelet transform using both the wavelets have been performed and compared to the Discrete Wavelet Transform technique. Mallet Algorithm based fast wavelet analysis makes the use of extension of a given finite-length signal and removes the border effects due to convolution. Image quality is measured objectively, using peak signal-to-noise ratio or picture quality scale, and subjectively, using perceived image quality. These results provide a good reference for application developers to choose a good wavelet compression system for their application.