Row-Centric Lossless Compression of Markov Images

Motivated by the question of whether the recently introduced Reduced Cutset Coding (RCC) offers rate-complexity performance benefits over conventional context-based conditional coding for sources with two-dimensional Markov structure, this paper compares several row-centric coding strategies that vary in the amount of conditioning as well as whether a model or an empirical table is used in the encoding of blocks of rows. The conclusion is that, at least for sources exhibiting low-order correlations, 1-sided model-based conditional coding is superior to the method of RCC for a given constraint on complexity, and conventional context-based conditional coding is nearly as good as the 1-sided model-based coding.Comment: submitted to ISIT 201

Lossless Text Image Compression using Two Dimensional Run Length Encoding

Text images are used in many types of conventional data communication where texts are not directly represented by digital character such as ASCII but represented by an image, for instance facsimile file or scanned documents. We propose a combination of Run Length Encoding (RLE) and Huffman coding for two dimensional binary image compression namely 2DRLE. Firstly, each row in an image is read sequentially. Each consecutive recurring row is kept once and the number of occurrences is stored. Secondly, the same procedure is performed column-wise to the image produced by the first stage to obtain an image without consecutive recurring row and column. The image from the last stage is then compressed using Huffman coding. The experiment shows that the 2DRLE achieves a higher compression ratio than conventional Huffman coding for image by achieving more than 8:1 of compression ratio without any distortion

Progressive transmission of pseudo-color images. Appendix 1: Item 4

The transmission of digital images can require considerable channel bandwidth. The cost of obtaining such a channel can be prohibitive, or the channel might simply not be available. In this case, progressive transmission (PT) can be useful. PT presents the user with a coarse initial image approximation, and then proceeds to refine it. In this way, the user tends to receive information about the content of the image sooner than if a sequential transmission method is used. PT finds application in image data base browsing, teleconferencing, medical and other applications. A PT scheme is developed for use with a particular type of image data, the pseudo-color or color mapped image. Such images consist of a table of colors called a colormap, plus a 2-D array of index values which indicate which colormap entry is to be used to display a given pixel. This type of image presents some unique problems for a PT coder, and techniques for overcoming these problems are developed. A computer simulation of the color mapped PT scheme is developed to evaluate its performance. Results of simulation using several test images are presented