Restoration of halftoned color-quantized images using linear estimator

Centre for Multimedia Signal Processing, Department of Electronic and Information EngineeringRefereed conference paper2006-2007 > Academic research: refereed > Refereed conference paperVersion of RecordPublishe

A General scheme for dithering multidimensional signals, and a visual instance of encoding images with limited palettes

AbstractThe core contribution of this paper is to introduce a general neat scheme based on soft vector clustering for the dithering of multidimensional signals that works in any space of arbitrary dimensionality, on arbitrary number and distribution of quantization centroids, and with a computable and controllable quantization noise. Dithering upon the digitization of one-dimensional and multi-dimensional signals disperses the quantization noise over the frequency domain which renders it less perceptible by signal processing systems including the human cognitive ones, so it has a very beneficial impact on vital domains such as communications, control, machine-learning, etc. Our extensive surveys have concluded that the published literature is missing such a neat dithering scheme. It is very desirable and insightful to visualize the behavior of our multidimensional dithering scheme; especially the dispersion of quantization noise over the frequency domain. In general, such visualization would be quite hard to achieve and perceive by the reader unless the target multidimensional signal itself is directly perceivable by humans. So, we chose to apply our multidimensional dithering scheme upon encoding true-color images – that are 3D signals – with palettes of limited sets of colors to show how it minimizes the visual distortions – esp. contouring effect – in the encoded images

Color quantization and image analysis

The aim of this paper is to provide an up-to-date review of the numerous aspects and technics of color quantization used in image analysis. This synthesis is all the more necessary that this field of study is in high expansion . In this article, we also propose several criteria and study parameters linked to visual analysis in order to improve the existing color quantization methods or to define new more accurate methods.L'objectif de cet article est de faire une synthèse sur les multiples aspects et techniques de quantification couleur développés en analyse d'image. Cette synthèse s'avère d'autant plus nécessaire que cette voix de recherche est en plein essor et que de multiples techniques peuvent être utilisées. Cet article propose également plusieurs critères et paramètres d'étude, fondés sur l'analyse visuelle, afin d'améliorer les méthodes de quantification couleur existantes ou de définir de nouvelles méthodes plus pertinentes

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