A novel approach for coding color quantized images

Advanced screen content coding using color table and index map

This paper presents an advanced screen content coding solution using color table and index map (cTIM) method. This cTIM scheme is implemented in the range extensions of high-efficiency video coding (HEVC-RExt) as an additional tool of intracoding to complement conventional spatial angular prediction to better exploit the screen content redundancy. For each coding unit, a number of major colors will be selected to form the color table, then the original pixel block is translated to the corresponding index map. A 1D or hybrid 1D/2D string match scheme is introduced to derive matched pairs of index map for better compression. Leveraging the color distribution similarity between neighboring image blocks, color table merge is developed to carry it implicitly. For those blocks that color table has to be signaled explicitly, intertable color sharing and intratable color differential predictive coding are applied to reduce the signaling overhead. Extensive experiments have been performed and they have demonstrated the significant coding efficiency improvement over conventional HEVC-RExt, resulting in 26%, 18%, and 15% bit rate reduction at lossless case and 23%, 19%, and 13% Bjontegaard Delta-rate improvement at lossy scenario of typical screen content with text and graphics, for respective all intra, random access, and low-delay using B picture encoder settings. Detailed performance study and complexity analysis (as well as the comparison with other algorithms) have been included as well to evidence the efficiency of proposed algorithm.

Self organizing motor maps for color-mapped image re-indexing

Palette re-ordering is an effective approach for improving the compression of color-indexed images. If the spatial distribution of the indexes in the image is smooth, greater compression ratios may be obtained. As is already known, obtaining an optimal re-indexing scheme is not a trivial task. In this paper, we provide a novel algorithm for palette re-ordering problem making use of a motor map neural network. Experimental results show the real effectiveness of the proposed method both in terms of compression ratio and zero-order entropy of local differences. Also, its computational complexity is competitive with previous works in the field.

An efficient re-indexing algorithm for color-mapped images

The efficiency of lossless compression algorithms for fixed-palette images (indexed images) may change if a different indexing scheme is adopted. Many lossless compression algorithms adopt a differential-predictive approach. Hence, if the spatial distribution of the indexes over the image is smooth, greater compression ratios may be obtained. Because of this, finding an indexing scheme that realizes such a smooth distribution is a relevant issue. Obtaining an optimal re-indexing scheme is suspected to be a hard problem and only approximate solutions have been provided in literature. In this paper, we restate the re-indexing problem as a graph optimization problem: an optimal re-indexing corresponds to the heaviest Hamiltonian path in a weighted graph. It follows that any algorithm which finds a good approximate solution to this graph-theoretical problem also provides a good re-indexing. We propose a simple and easy-to-implement approximation algorithm to find such a path. The proposed technique compares favorably with most of the algorithms proposed in literature, both in terms of computational complexity and of compression ratio.

A survey on palette reordering methods for improving the compression of color-indexed images

Palette reordering is a well-known and very effective approach for improving the compression of color-indexed images. In this paper, we provide a survey of palette reordering methods, and we give experimental results comparing the ability of seven of them in improving the compression efficiency of JPEG-LS and lossless JPEG 2000. We concluded that the pairwise merging heuristic proposed by Memon et al. is the most effective, but also the most computationally demanding. Moreover, we found that the second most effective method is a modified version of Zeng's reordering technique, which was 3%-5% worse than pairwise merging, but much faster.

Color quantization and processing by Fibonacci lattices

Color quantization is sampling of three-dimensional (3-D) color spaces (such as RGB or Lab) which results in a discrete subset of colors known as a color codebook or palette. It is extensively used for display, transfer, and storage of natural images in Internet-based applications, computer graphics, and animation. We propose a sampling scheme which provides a uniform quantization of the Lab space. The idea is based on several results from number theory and phyllotaxy. The sampling algorithm is very much systematic and allows easy design of universal (image-independent) color codebooks for a given set of parameters. The codebook structure allows fast quantization and ordered dither of color images. The display quality of images quantized by the proposed color codebooks is comparable with that of image-dependent quantizers. Most importantly, the quantized images are more amenable to the type of processing used for grayscale ones. Methods for processing grayscale images cannot be simply extended to color images because they rely on the fact that each gray-level is described by a single number and the fact that a relation of full order can be easily established on the set of those numbers. Color spaces (such as RGB or Lab) are, on the other hand, 3-D. The proposed color quantization, i.e., color space sampling and numbering of sampled points, makes methods for processing grayscale images extendible to color images. We illustrate possible processing of color images by first introducing the basic average and difference operations and then implementing edge detection and compression of color quantized images.

The LOCO-I lossless image compression algorithm: principles and standardization into JPEG-LS

LOCO-I (LOw COmplexity LOssless COmpression for Images) is the algorithm at the core of the new ISO/ITU standard for lossless and near-lossless compression of continuous-tone images, JPEG-LS. It is conceived as a "low complexity projection" of the universal context modeling paradigm, matching its modeling unit to a simple coding unit. By combining simplicity with the compression potential of context models, the algorithm "enjoys the best of both worlds." It is based on a simple fixed context model, which approaches the capability of the more complex universal techniques for capturing high-order dependencies. The model is tuned for efficient performance in conjunction with an extended family of Golomb-type codes, which are adaptively chosen, and an embedded alphabet extension for coding of low-entropy image regions. LOCO-I attains compression ratios similar or superior to those obtained with state-of-the-art schemes based on arithmetic coding. Moreover, it is within a few percentage points of the best available compression ratios, at a much lower complexity level. We discuss the principles underlying the design of LOCO-I, and its standardization into JPEC-LS.

Digital color imaging

This paper surveys current technology and research in the area of digital color imaging. In order to establish the background and lay down terminology, fundamental concepts of color perception and measurement are first presented using vector-space notation and terminology. Present-day color recording and reproduction systems are reviewed along with the common mathematical models used for representing these devices. Algorithms for processing color images for display and communication are surveyed, and a forecast of research trends is attempted. An extensive bibliography is provided.

On ordering color maps for lossless predictive coding

Linear predictive techniques perform poorly when used with color-mapped images where pixel values represent indices that point to color values in a look-up table. Reordering the color table, however, can lead to a lower entropy of prediction errors. In this paper, we investigate the problem of ordering the color table such that the absolute sum of prediction errors is minimized. The problem turns out to be intractable, even for the simple case of one-dimensional (1-D) prediction schemes. We give two heuristic solutions for the problem and use them for ordering the color table prior to encoding the image by lossless predictive techniques. We demonstrate that significant improvements in actual bit rates can be achieved over dictionary-based coding schemes that are commonly employed for color-mapped images.