Error Modeling for Hierarchical Lossless Image Compression

(c) 1992 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works.We present a new method for error modeling applicable to the MLP algorithm for hierarchical lossless image compression. This method, based on a concept called the variability index, provides accurate models for pixel prediction errors without requiring explicit transmission of the models. We also use the vari- ability index to show that prediction errors do not always follow the Laplace distribution, as is commonly assumed; replacing the Laplace distribution with a more general symmetric exponential distribution further improves compression. We describe a new compression measurement called compression gain, and we give experimental results showing that the MLP method using the variability index technique for error modeling gives signi cantly more compression gain than other methods in the literature

Fast Progressive Lossless Image Compression

Copyright 1994 Society of Photo-Optical Instrumentation Engineers. One print or electronic copy may be made for personal use only. Systematic electronic or print reproduction and distribution, duplication of any material in this paper for a fee or for commercial purposes, or modification of the content of the paper are prohibited. http://dx.doi.org/10.1117/12.173910We present a method for progressive lossless compression of still grayscale images that combines the speed of our earlier FELICS method with the progressivity of our earlier MLP method We use MLP s pyramid based pixel sequence and image and error modeling and coding based on that of FELICS In addition we introduce a new pre x code with some advantages over the previously used Golomb and Rice codes Our new progressive method gives compression ratios and speeds similar to those of non progressive FELICS and those of JPEG lossless mode also a non progressive method The image model in Progressive FELICS is based on a simple function of four nearby pixels We select two of the four nearest known pixels using the two with the middle non extreme values Then we code the pixel s intensity relative to the selected pixels using single bits adjusted binary codes and simple pre x codes like Golomb codes Rice codes or the new family of pre x codes introduced here We estimate the coding parameter adaptively for each context the context being the absolute value of the di erence of the predicting pixels we adjust the adaptation statistics at the beginning of each level in the progressive pixel sequenc

Parallel Lossless Image Compression Using Huffman and Arithmetic Coding

We show that high-resolution images can be encoded and decoded e ciently in parallel. We present an algorithm based on the hierarchical MLP method, used either with Hu man coding or with a new variant of arithmetic coding called quasi-arithmetic coding. The coding step can be parallelized, even though the codes for di erent pixels are of di erent lengths; parallelization of the prediction and error modeling components is straightforward

Losslees compression of RGB color images

Although much work has been done toward developing lossless algorithms for compressing image data, most techniques reported have been for two-tone or gray-scale images. It is generally accepted that a color image can be easily encoded by using a gray-scale compression technique on each of the three accounts the substantial correlations that are present between color planes. Although several lossy compression schemes that exploit such correlations have been reported in the literature, we are not aware of any such techniques for lossless compression. Because of the difference in goals, the best way of exploiting redundancies for lossy and lossless compression can be, and usually are, very different. We propose and investigate a few lossless compression schemes for RGB color images. Both prediction schemes and error modeling schemes are presented that exploit inter-frame correlations. Implementation results on a test set of images yield significant improvements

Error Modeling for Hierarchical Lossless Image Compression

We present a new method for error modeling applicable to the MLP algorithm for hierarchical lossless image compression. This method, based on a concept called the variability index, provides accurate models for pixel prediction errors without requiring explicit transmission of the models. We also use the variability index to show that prediction errors do not always follow the Laplace distribution, as is commonly assumed; replacing the Laplace distribution with a more general symmetric exponential distribution further improves compression. We describe a new compression measurement called compression gain, and we give experimental results showing that the MLP method using the variability index technique for error modeling gives significantly more compression gain than other methods in the literature