Modeling using 2-D areas of ideal cross-point regions for lossless images compression

Abstract

This paper presents 2-D areas of ideal cross-point regions which are the new part in the theory of cross-point regions. Actually for using cross-point regions we need an algorithm for determining cross-point maps; this takes a long time and a big space for storing these maps, and brings about not high compression ratio when using one dimensional cross-point regions because many coordinates of data points need to be saved for decoding. When these 2-D areas are used, the scheme of 2-DICRIC (2-D Ideal Cross-point Regions for lossless Image Compression) for losslessly encoding and decoding images with the optimization of probability of cross points which are neighbor to the points of grey levels 2ⁿ is improved to get higher compression ratio. The base idea of this method is the effect of Gray coding on cross points, and there are many cross-point regions. Before Gray coding data sets of cross points are determined, they are called the ideal cross point regions (ICRs). After Gray coding these regions always contain only 1 bits or 0 bits depending on the number of bit plane after the operation of bit plane decomposition. This is the characteristic of images, the data do not change much in a specific area, especially in medical images which have many regions with the approximate grey levels. So, the way to determine 2-D areas of cross-point regions so that the cross-point maps are small is important for the theory. The theory with these 2-D areas has important effects on the compression ratio when encoding and decoding processes of lossless image compression for data transmission are proceeded.