Image compression using wavelet packet and singular value decomposition

Compression of digital images has been a topic of research for many years and a number of image compression standards have been created for different applications. The role of compression is to reduce bandwidth requirements for transmission and memory requirements for storage of all forms of data. The main objective is to study and implement the operations used in a lossy compression scheme to compress two-dimensional images. Basically, this scheme consists of three operations, which are the transform, quantization and entropy encoding operations. Wavelet Transform and Wavelet Packet Transform are efficient tools to represent the image. Wavelet Packet Transform is a generalization of Wavelet Transform which is more adaptive than the Wavelet Transform because it offers a rich library of bases from which the best one can be chosen for a certain class of images with a specified cost function. Wavelet Packet decomposition yields a redundant representation of the image. In this work, Singular Value Decomposition is used as a tool to select the best basis. After selecting the best tree, the coefficients of the best tree are quantized using dead zone quantization. To reduce the number of bits required to transmit the indexes of the codeword, a lossless Huffman algorithm was implemented as the final stage of the encoding process. To reconstruct the compressed image, the operations are reserved. The simulation result reveals that, the quantity of the image is good even though the compression ratio is increased due to reduction in Wavelet Packet sub-bands.