Color quantization method based on principal component analysis and linear discriminant analysis for palette-based image generation

Color quantization method based on principal component analysis and linear discriminant analysis... High performance of color quantization processing is very important for obtaining limited-color images with good quality. The median cut algorithm (MCA) is a typical color quantization method. Its computational cost is low owing to its simple algorithm, but the quality of output images is mediocre at best. In this paper, we describe a modification of MCA. In our method, we use a combination of principal component analysis (PCA) and linear discriminant analysis (LDA) to accomplish effective partitioning of color space. Concretely, PCA and LDA are used to calculate partitioning planes and their positions, respectively. We verify the effectiveness of our method through experiments using 24-bit full-color natural images. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Optical Review Springer Journals

Color quantization method based on principal component analysis and linear discriminant analysis for palette-based image generation

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Publisher
Springer Japan
Copyright
Copyright © 2017 by The Optical Society of Japan
Subject
Physics; Optics, Lasers, Photonics, Optical Devices; Atomic, Molecular, Optical and Plasma Physics; Quantum Optics; Microwaves, RF and Optical Engineering
ISSN
1340-6000
eISSN
1349-9432
D.O.I.
10.1007/s10043-017-0376-1
Publisher site
See Article on Publisher Site

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