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A new nonlinear quantizer for image processing within nonextensive statistics

Authors
Journal
Physica A Statistical Mechanics and its Applications
0378-4371
Publisher
Elsevier
Publication Date
Volume
381
Identifiers
DOI: 10.1016/j.physa.2007.03.028
Keywords
  • Image Processing
  • Nonlinear Quantization
  • Tsallis Statistics

Abstract

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).

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