A new nonlinear quantizer for image processing within nonextensive statistics

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Date

2007

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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). © 2007 Elsevier B.V. All rights reserved.

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Keywords

Error analysis , Statistical mechanics , Vector quantization , Image quantization , Nonlinear quantization , Quantization errors , Tsallis statistics , Image processing

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http://akademikarsiv.cbu.edu.tr:4000/handle/123456789/19265

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