Embedded Image Coding Using Wavelet Difference Reduction
Jun Tian, Raymond O. Wells · Kluwer Academic Publishers eBooks · 2006
We present an embedded image coding method, which basically consists of three steps, Discrete Wavelet Transform, Differential Coding , and Binary Reduction . Both J. Shapiro’s embedded zerotree wavelet algorithm, and A. Said and W. A. Pearlman’s codetree algorithm use spatial orientation tree structures to implicitly locate the significant wavelet transform coefficients. Here a direct approach to find the positions of these significant coefficients is presented. The encoding can be stopped at any point, which allows a target rate or distortion metric to be met exactly. The bits in the bit stream are generated in the order of importance, yielding a fully embedded code to successively approximate the original image source; thus it’s well suited for progressive image transmission. The decoder can also terminate the decoding at any point, and produce a lower (bit) rate reconstruction image. Our algorithm is very simple in its form (which will make the encoding and decoding very fast), requires no training of any kind or prior knowledge of image sources, and has a clear geometric structure. The image coding results of it are quite competitive with almost all previous reported image compression algorithms on standard test images. These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.