A new nonlinear quantizer for image processing within nonextensive statistics
| Autor: | Ozhan Kayacan, Ilker Kilic |
|---|---|
| Rok vydání: | 2007 |
| Předmět: |
Statistics and Probability
Lossless compression Theoretical computer science Quantization (signal processing) Tsallis entropy Tsallis statistics ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION Vector quantization Image processing Data_CODINGANDINFORMATIONTHEORY Lossy compression Condensed Matter Physics Nonlinear system Algorithm Mathematics |
| Zdroj: | Physica A: Statistical Mechanics and its Applications. 381:420-430 |
| ISSN: | 0378-4371 |
| Popis: | 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). |
| Databáze: | OpenAIRE |
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