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.