Fast Color Quantization Using Weighted Sort-Means Clustering

Color quantization is an important operation with numerous applications in graphics and image processing. Most quantization methods are essentially based on data clustering algorithms. However, despite its popularity as a general purpose clustering algorithm, k-means has not received much respect in the color quantization literature because of its high computational requirements and sensitivity to initialization. In this paper, a fast color quantization method based on k-means is presented. The method involves several modifications to the conventional (batch) k-means algorithm including data reduction, sample weighting, and the use of triangle inequality to speed up the nearest neighbor search. Experiments on a diverse set of images demonstrate that, with the proposed modifications, k-means becomes very competitive with state-of-the-art color quantization methods in terms of both effectiveness and efficiency.Comment: 30 pages, 2 figures, 4 table

Hierarchical Color Quantization with a Neural Gas Model Based on Bregman Divergences

In this paper, a new color quantization method based on a self-organized artificial neural network called the Growing Hierarchical Bregman Neural Gas (GHBNG) is proposed. This neural network is based on Bregman divergences, from which the squared Euclidean distance is a particular case. Thus, the best suitable Bregman divergence for color quantization can be selected according to the input data. Moreover, the GHBNG yields a tree-structured model that represents the input data so that a hierarchical color quantization can be obtained, where each layer of the hierarchy contains a different color quantization of the original image. Experimental results confirm the color quantization capabilities of this approach.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech

Color quantization of compressed video sequences

Centre for Multimedia Signal Processing, Department of Electronic and Information Engineering2002-2003 > Academic research: refereed > Publication in refereed journalVersion of RecordPublishe

A New Spin on Color Quantization

We address the problem of image color quantization using a Maximum Entropy based approach. Focusing on pixel mapping we argue that adding thermal noise to the system yields better visual impressions than that obtained from a simple energy minimization. To quantify this observation, we introduce the coarse-grained quantization error, and seek the optimal temperature which minimizes this new observable. By comparing images with different structural properties, we show that the optimal temperature is a good proxy for complexity at different scales. Finally, noting that the convoluted error is a key observable, we directly minimize it using a Monte Carlo algorithm to generate a new series of quantized images. Adopting an original approach based on the informativity of finite size samples, we are able to determine the optimal convolution parameter leading to the best visuals.Comment: 8 pages, 5 figure

Improvements to the color quantization process

The presentation of color images on devices with limited color capabilities requires a reduction in the number of colors contained in the images. Color image quantization is the process of reducing the number of colors used in an image while maintaining its appearance as much as possible. This reduction is performed using a color image quantization algorithm. The quantization algorithm attempts to select k colors that best represent the contents of the image. The original image is then recolored using the representative colors. to improve the resulting image, a dithering process can be used in place of the recoloring.;This dissertation deals with several areas of the color image quantization process. The main objective, however, is new or improved algorithms for the production of images with a better visual quality than those produced by existing algorithms while maintaining approximately the same running time. First, a new algorithm is developed for the selection of the representative color set. The results produced by the new algorithm are better both visually and quantitatively when compared to existing algorithms. Second, a new nearest-neighbor search algorithm that is based on the Locally Sorted Search algorithm is developed to reduce the time required to map the input colors to a representative color. Finally, two modifications are made to the error-diffusion dithering technique that improve the execution time. These modifications include the use of a two-weight matrix for the distribution of the error values and the presentation of a method to parallelize the error-diffusion technique. Furthermore, the analytical results of several experiments are provided to show the effectiveness each of these additions and improvements