A new nonlinear quantizer for image processing within nonextensive statistics

Ilker Kilic and Ozhan Kayacan

Physica A: Statistical Mechanics and its Applications, 2007, vol. 381, issue C, 420-430

Abstract: In this study, we introduce a new nonlinear quantizer for image processing by using Tsallis entropy. Lloyd–Max quantizer is commonly used in minimizing the quantization errors. We report that the new introduced technique works better than Lloyd–Max one for selected standard images and could be an alternative way to minimize the quantization errors for image processing. We, therefore, hopefully expect that the new quantizer could be a useful tool for all the remaining process after image quantization, such as coding (lossy and lossless compression).

Keywords: Image processing; Nonlinear quantization; Tsallis statistics (search for similar items in EconPapers)
Date: 2007
References: View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:phsmap:v:381:y:2007:i:c:p:420-430

DOI: 10.1016/j.physa.2007.03.028

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