TMW - a new method for lossless image compression
Bernd T. Meyer, Peter Eric Tischer · 1997
We present a general purpose lossless greyscale image compression method, TMW, that is based on the use of linear predictors and implicit segmentation. In order to achieve competitive compression, the compression process is split into an analysis step and a coding step. In the rst step, a set of linear predictors and other parameters suitable for the image is calculated, which is included inthecompressed le and subsequently used for the coding step. This adaption allows TMW to perform well over a very wide range of image types. Other signi cant features of TMW are the use of a one-parameter probability distribution, probability calculations based on unquantized prediction values, blending of multiple probability distributions instead of prediction values, and implicit image segmentation. The method has applications beyond image compression. The work is also relevant to image segmentation and image comparison. For image compression, the method has been compared to CALIC on a selection of test images, and typically outperforms it by between 2 and 10 percent, at the cost of considerably slower compression. In particular, a bitrate of less than 3.92 bpp has been achieved for the luminance band of the well known lenna image, compared to 4.05 bpp reported for CALIC in [Wu97]. 1