Journal Article10.1080/03052150601127958
Colour map design through optimization
Lars Nolle,Gerald Schaefer +1 more
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TL;DR: In this work a variant of simulated annealing (SA) was employed as a standard black-box optimization algorithm for colour map generation and is shown to outperform all other algorithms and hence to provide images with superior image quality.
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Abstract: Often in engineering systems, full-colour images have to be displayed on limited hardware, for example on mobile devices or embedded systems that can only handle a limited number of colours. Therefore an image is converted into an indexed map from where the indices point to specific colours in a fixed-size colour map generated for that image. The choice of an optimal colour map, or palette, is therefore crucial as it directly determines the quality of the resulting image. Typically, standard quantization algorithms are used to create colour maps. Whereas these algorithms employ domain specific knowledge, in this work a variant of simulated annealing (SA) was employed as a standard black-box optimization algorithm for colour map generation. The main advantage of black-box optimization algorithms is that they do not require any domain specific knowledge yet are able to provide a near optimal solution. The effectiveness of the approach is evaluated by comparing its performance with several specialized colour...
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Citations
Fuzzy clustering for colour reduction in images
Gerald Schaefer,Huiyu Zhou +1 more
TL;DR: Experimental results show that fuzzy c-means performs significantly better than other, purpose built colour quantisation algorithms, and also confirm that the fast fuzzy clustering algorithms provide similar quantisation results to the full conventional fuzzy c -means approach.
59
Fast color quantization using MacQueen’s k-means algorithm
TL;DR: A novel CQ method based on an online k-means formulation due to MacQueen that utilizes adaptive and efficient cluster center initialization and quasirandom sampling to attain deterministic, high speed, and high-quality quantization.
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Rough C-means and Fuzzy Rough C-means for Colour Quantisation
TL;DR: Experimental results on a standard set of images show that these approaches performs significantly better than other, purpose built colour quantisation algorithms.
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A Hybrid Color Quantization Algorithm Incorporating a Human Visual Perception Model
Gerald Schaefer,Lars Nolle +1 more
- 01 Nov 2015
TL;DR: This article introduces a color quantization algorithm that hybridizes an optimization scheme based with an image quality metric that mimics the human visual system and employs a variant of simulated annealing with the objective function describing the S‐CIELAB image quality of the quantized image compared with its original.
11
Soft computing-based colour quantisation
TL;DR: An overview of recent soft computing approaches to the colour quantisation problem is presented and it is demonstrated how optimisation-based colourquantisation can be employed in conjunction with a more appropriate measure for image quality.
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An Algorithm for Vector Quantizer Design
Y. Linde,A. Buzo,Robert M. Gray +2 more
TL;DR: An efficient and intuitive algorithm is presented for the design of vector quantizers based either on a known probabilistic model or on a long training sequence of data.
Color image quantization for frame buffer display
TL;DR: Algorithms for adaptive, tapered quantization of color images are described and the research is motivated by the desire to display high-quality reproductions ofcolor images with small frame buffers.
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