Department of Data Science and Engineering, Silesian University of Technology, Akademicka 16, 44-100 Gliwice, Poland.
Sensors (Basel). 2023 Jan 18;23(3):1108. doi: 10.3390/s23031108.
Nature-inspired artificial intelligence algorithms have been applied to color image quantization (CIQ) for some time. Among these algorithms, the particle swarm optimization algorithm (PSO-CIQ) and its numerous modifications are important in CIQ. In this article, the usefulness of such a modification, labeled IDE-PSO-CIQ and additionally using the idea of individual difference evolution based on the emotional states of particles, is tested. The superiority of this algorithm over the PSO-CIQ algorithm was demonstrated using a set of quality indices based on pixels, patches, and superpixels. Furthermore, both algorithms studied were applied to superpixel versions of quantized images, creating color palettes in much less time. A heuristic method was proposed to select the number of superpixels, depending on the size of the palette. The effectiveness of the proposed algorithms was experimentally verified on a set of benchmark color images. The results obtained from the computational experiments indicate a multiple reduction in computation time for the superpixel methods while maintaining the high quality of the output quantized images, slightly inferior to that obtained with the pixel methods.
受自然启发的人工智能算法已经在彩色图像量化 (CIQ) 中得到了一段时间的应用。在这些算法中，粒子群优化算法 (PSO-CIQ) 及其众多变体在 CIQ 中非常重要。在本文中，测试了一种这样的变体，标记为 IDE-PSO-CIQ，并额外使用了基于粒子情绪的个体差异进化的思想。使用基于像素、补丁和超像素的一组质量指标证明了该算法相对于 PSO-CIQ 算法的优越性。此外，研究了两种算法都应用于超像素量化图像版本，在更短的时间内创建颜色调色板。提出了一种启发式方法来根据调色板的大小选择超像素的数量。所提出的算法在一组基准彩色图像上进行了实验验证。计算实验的结果表明，超像素方法的计算时间大大减少，同时保持输出量化图像的高质量，略低于像素方法获得的质量。
