EMBEDDED ZERO TREE IMAGE CODING USING MORTON SCANNING OF WAVELET COEFFICIENTS

Abstract

The embedded zerotree wavelet algorithm (EZW) is a simple, yet remarkably efficient, image compression algorithm, having the property that the bits in the bit stream are generated in the order of importance, yielding a fully embedded code. The embedded code represents a sequence of binary decisions that distinguish an image from the null image. Using an embedded coding algorithm, an encoder can terminate the encoding at any point thereby allowing a target rate or target distortion matrix to be met easily. Also, given.a bit-stream, the decoder can cease decoding at any poiht in the bit-stream and still produces exactly the same image that would have been encoded at the bit rate corresponding to the truncated bit stream In addition to producing a fully embedded bit-stream, EZW consistently produces compression results that are competitive with virtually all known compression algorithms on standard test images. Yet this performance is achieved with a technique that requires absolutely no training, no pre-stored tables or codebooks, and requires no prior knowledge of the image source. The EZW algorithm is based on four key concepts: 1) a discrete wavelet transform or hierarchal subband decomposition, 2) prediction of the absence of significant information across scales by exploiting the self-similarity inherent in images, 3) entropy —coded successive —approximation quantization, and 4) universal lossless data compression which is achieved via arithmetic coding. In this thesis, EZW algorithm (using Morton scanning) is implemented and analyzed using other standard algorithms of Image compression for its performance and behavior. The software is written in C only for 8 bit grayscale images and compiled with the Microsoft Visual C++ 6.0 package.