A new nonlinear quantizer for image processing within nonextensive statistics
| dc.contributor.author | Kilic I. | |
| dc.contributor.author | Kayacan O. | |
| dc.date.accessioned | 2025-04-10T11:16:46Z | |
| dc.date.available | 2025-04-10T11:16:46Z | |
| dc.date.issued | 2007 | |
| dc.description.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. | |
| dc.identifier.DOI-ID | 10.1016/j.physa.2007.03.028 | |
| dc.identifier.uri | http://hdl.handle.net/20.500.14701/52090 | |
| dc.title | A new nonlinear quantizer for image processing within nonextensive statistics | |
| dc.type | Article |