<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.
Images contain large amount of data and are used in many applications.
Not only compressed image data save space, but also in certain applications
such as the World-Wide-Web (WWW),
they save time since the amount of data to be transmitted is smaller than the original image.
Codebook approaches have been used in lossy image compression.
For example, vector quantization employs codebook.
For lossless compression of images, more than a decade ago,
a codebook approach based on spanning trees was proposed.
In this work we investigate suitability of permutation codes for still images.
Palette images are widely used on the World Wide Web (WWW) and in game-cartridge applications. Many images used on the WWW are stored and transmitted after they are compressed losslessly with the standard graphics interchange format (GIF), or portable network graphics (PNG). Well-known 2-D compression schemes, such as JPEG-LS and JPEG-2000, fail to yield better compression than GIF or PNG due to the fact that the pixel values represent indices that point to color values in a look-up table. To improve the compression performance of JPEG-LS and JPEG-2000 techniques, several researchers have proposed various reindexing algorithms. We investigate various compression techniques for color palette images. We propose a new technique comprised of a traveling salesman problem (TSP)-based reindexing scheme, Burrows-Wheeler transformation, and inversion ranks. We show that the proposed technique yields better compression gain on average than all the other 1-D compressors and the reindexing schemes that utilize JPEG-LS or JPEG-2000.
In a pseudo-color (color-mapped) image pixel values represent indices that point to color values in a look-up table. Well-known linear predictive schemes, such as JPEG and CALIC, perform poorly when used with pseudo-color images, while universal compressors, such as Gzip, Pkzip and Compress, yield better compression gain. Recently, Burrows and Wheeler introduced the Block Sorting Lossless Data Compression Algorithm (BWA). The BWA algorithm received considerable attention. It achieves compression rates as good as context-based methods, such as PPM, but at execution speeds closer to Ziv-Lempel techniques. The BWA algorithm is mainly composed of a block-sorting transformation which is known as Burrows-Wheeler Transformation (BWT), followed by Move-To-Front coding. In this paper, we introduce a new block transformation, Linear Order Transformation (LOT). We delineate its relationship to BWT and show that LOT is faster than BWT transformation. We then show that when MTF coder is employed after the LOT, the compression gain obtained is better than the well-known compression techniques, such as GIF, JPEG, CALLIC, Gzip, LZW (Unix Compress) and the BWA for pseudo-color images.
Linear prediction schemes, such as JPEG or BJPEG, are simple and normally result in a significant reduction in source entropy. Occasionally the entropy of the prediction error becomes greater than that of the original image. Such situations frequently occur when the image data has discrete gray-levels located within certain intervals. To alleviate this problem, various authors have suggested different preprocessing methods. However, the techniques reported requires two-pass. In this paper, we extend the definition of Lehmer-type inversions from premutations to multiset permutations and present a one-pass algorithm based on inversions of a multiset permutation. We obtain comparable results when we applied JPEG and even better result when we applied BJPEG on preprocessed image, which is treated as multiset permutation.