Comparison of image palette trimming techniques for segmentation task

DOI: 10.31673/2786-8362.2023.011111

Abstract

This paper offers a comparison of palette quantization techniques for image segmentation task, raster image vectorization optimization purpose. Image color spaces are reviewed. The elaborated methods are characterized, their strong side and weaknesses are pointed out, the application advisability of listed methods are analyzed.
Image segmentation is a highly demanded task in lots of areas requiring application of advanced algorithms both for shape recognition and color processing. Color quantization continues to be a subject of active research. The latest research is aimed at optimizing existing algorithms and finding new methodologies. The significant progress in the field of digital technologies in cooperation with artificial intelligence technologies solves old problems and raises new ones. The trade-off between computational efficiency and segmentation accuracy is still a key challenge, especially in real-time applications. In addition, the adaptation of these methods to various fields, such as medical industry, video assistants in sports, analysis of space images, surveillance systems, creates challenges for future research.
Purpose: research of color quantization methods, their efficiency and applicability for image segmentation tasks solving.

Keywords: segmentation, vectorization, quantizing, clustering, palette, color, color space.

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