<p>Among the variety of multimedia formats, color images play a prominent role. A technique for lossless compression of color images is introduced. The technique is composed of first transforming a red, green, and blue image into luminance and chrominance domain (Y<italic>C</italic><sub>u</sub><italic>C</italic><sub>v</sub>). Then, the luminance channel <italic>Y</italic> is compressed with a context-based, adaptive, lossless image coding technique (CALIC). After processing the chrominance channels with a hierarchical prediction technique that was introduced earlier, Burrows–Wheeler inversion coder or JPEG 2000 is used in compression of those <italic>C</italic><sub>u</sub> and <italic>C</italic><sub>v</sub> channels. It is demonstrated that, on a wide variety of images, particularly on medical images, the technique achieves substantial compression gains over other well-known compression schemes, such as CALIC, M-CALIC, Better Portable Graphics, JPEG-LS, JPEG 2000, and the previously proposed hierarchical prediction and context-adaptive coding technique LCIC.</p>
In this work, we propose a method that utilizes a new context model along with a pseudo-distance technique in
compression of color-mapped images. Graphic Interchange Format (GIF) and Portable Network Graphics (PNG) are two
of the well-known and frequently used techniques for the compression of color-mapped images. There are several
techniques that achieve better compression results than GIF and PNG; however, most of these techniques need two
passes on the image data, while others do not run in linear time. The pseudo-distance technique runs in linear time and
requires only one pass. We show that using the proposed context model along with the pseudo-distance technique yields
better results than both PNG and GIF.