ABSTRACT
This project work dealt with the compression of image data using wavelet technique.
This project work provides a fundamental, yet comprehensive coverage of the compression of
image data. It gives clear meaning and advantages of compression of image data. It also gives an
overview of wavelet technique of image data compression.
The necessity of image compression continuously grows during the last decade; one of the most
powerful and perspective approaches in this area is image compression using discrete wavelet
transform. The image compression includes transform of image, quantization and encoding. This
paper mainly describes the transform of image and quantization technique. The idea of Wavelet
Packet Tree is used to transform the still and colour images. This paper describes the new
approach to construct the best tree on the basis of Shannon entropy. Algorithm checks the
entropy of decomposed nodes (child nodes) with entropy of node, which has been decomposed
(parent node) and takes the decision of decomposition of a node. In addition, we have proposed
an adaptive thresholding for quantization, which is based on type of wavelet used and nature of
image. The proposed algorithm provides a good compression performance. Results are compared
in terms of percentage of zeros; percentage of energy retained and signals to noise ratio.