Monochrome Multitone Image Approximation on Lowered Dimension Palette with Sub-optimization Method Based on Genetic Algorithm

Abstract

The problem of sub-optimum approximation of monochrome multitone images (MMI) by a palette with reduced amount of tones, called support palette (SP), is solved. The SP palette tones are defined with the images analysis, which arise in related scientific topics as: technical sight, recognition of images, etc. In this work the research objective was to assess the opportunity of using efficiently such analysis, by applying the genetic algorithms (GA) for sub-optimum approximation of MMI, considering the original big size tones palette [1]. The proposed approximation consists in replacing the original MMI pixels with the approximated pixels from a smaller size tone palette. This procedure is of importance in the synthetic vision approach, where image recognition procedures are expected to define the main contours within the image. The developed method reduces the amount of tones used to display an image, whose approximation approach is presented in this paper. In order to solve it, two alternative problems are considered: (1) minimization of losses in such image transformation, and (2) minimization of the SP size (for example, to simplify the image recognition process). The approximated MMI quality is defined as the mean square deviation of pixels brightness (original to approximated). The chromosome in GA is SP, where tones are represented as genes. Such approximations are resulting from the mutational variation of the MMI palette tones, within gene alleles, which are formed by applying the original palette tones. The palette is iteratively changing from generation to generation, where the reduction of the stop risks is done on the local extremum. This fact increases the available search opportunities, as provided by the multi-point crossing-over algorithm, whose parameters are able to mutate during such an evolution process. In addition, to demonstrate the result of this work, an appropriate software has been developed, having an easy-to-use user interface, enabling to show the highly efficient processing of the investigated algorithm. The presented solutions are validated with photo examples of several technical objects, on which the sub-optimum method has been applied.