Optimal image quantization, perception and the median cut algorithm

Abstract

We study the perceptual problem related to image quantization from an optimization point of view, using different metrics on the color space. A consequence of the results presented is that quantization using histogram equalization provides optimal perceptual results. This fact is well known and widely used but, to our knowledge, a proof has never appeared on the literature of image processing.

Improved Exemplar based Image Inpainting for Partial Instance Occlusion Handling with K-means Clustering and YCbCr Color Space

“…In the paper [6][7][8][16][17][18], the need for color quantization has been discussed to achieve lesser time consumption and the resultant images can be further used for better optimization in results. As in [7][8][9], there is a need to remove redundant pixels that can improve the quantization process in color space. The PDE method has been used with K-means for quantization [17].…”

“…The color image quantization however, is ancillary but still remains very important in image clustering. It is a process to reduce the color distortion quantitatively in the image [6,7,9]. There have been conventional image clustering algorithms in literature and out of these numerous methods, K-means clustering happens to be the simplest and widely used method.…”

An Adaptive Quantizer for High Dynamic Range Content: Application to Video Coding

“…Meanwhile, 2 k codewords are fully used rather than part of them as employed by PQ. More perceptual information can be preserved by the efficient use of codewords [24]. A large n(j) indicates a small gap between two consecutive levels, and leads to the low reproducible contrast as calculated in Equation (3).…”

Análisis de la compresión de imágenes utilizando clustering bajo el enfoque de colonia de hormigas

“…Las imágenes de Lena que se presentan han sido cuantificadas utilizando el algoritmo Median Cut empleando 16, 8, 4 y 2 bits / píxel [Mota et al, 2001]. (Ver Gráfica 6).…”