Efficient, Edge-Aware, Combined Color Quantization and Dithering

Abstract

Abstract-In this paper we present a novel algorithm to simultaneously accomplish color quantization and dithering of images. This is achieved by minimizing a perception-based cost function which considers pixel-wise differences between filtered versions of the quantized image and the input image. We use edge aware filters in defining the cost function to avoid mixing colors on opposite sides of an edge. The importance of each pixel is weighted according to its saliency. To rapidly minimize the cost function, we use a modified multi-scale iterative conditional mode (ICM) algorithm which updates one pixel a time while keeping other pixels unchanged. As ICM is a local method, careful initialization is required to prevent termination at a local minimum far from the global one. To address this problem, we initialize ICM with a palette generated by a modified mediancut method. Compared to previous approaches, our method can produce high quality results with fewer visual artifacts but also requires significantly less computational effort.Index Terms-Color quantization, dithering, optimizationbased image processing.

A new spin on color quantization

“…This algorithmic approach is necessary as there is no explicit minimum of L θ (h, •). Let us stress that, while Monte Carlo approaches and other dithering methods have historically contributed to the image quantization problem, the last two decades were marked by more evolved developments, using adaptive kernels [33] and clustering algorithms [34,35]. Here, for the sake of physical interpretation, we focus our attention on the classic Monte Carlo approach as it contains the minimal ingredients to tackle this problem.…”

“…considering alternatives to the Euclidean distance such as perception based cost functions [33], structural similarity metrics [37], quality indices [35], distances including transport terms [43] or edge detection [33], which take into account a priori the local arrangement of the pixels. within the Econophysics & Complex Systems Research Chair, under the aegis of the Fondation du Risque, the Fondation de l'Ecole polytechnique, the Ecole polytechnique and Capital Fund Management.…”

MODAN: Multifocal Object Detection Associative Network for Maritime Horizon Surveillance

“…Color quantization is a method that converts an image's colors into a limited set of colors, which maps 24-bit colors to a fixed number of colors. This not only reduces the number of colors in the image, which reduces the amount of computation required for image processing, but also has the advantage of removing noise caused by clouds or waves by eliminating high-frequency components from the image and generating smooth and consistent colors [22].…”