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). (c) 2007 Elsevier B.V. All rights reserved
Abstract—The global maximum of an entropy function with different decision levels for a three-level ...
This paper compares the effectiveness of the Tsallis entropy over the classic Boltzmann-Gibbs-Shanno...
By combining a maximum conditional entropy principle with a basic equation of (Shannon) information ...
The Boltzmann–Gibbs and Tsallis entropies are essential concepts in statistical physics, which have ...
In image processing, the maximum entropy principle is generally recognized as having a relevant role...
The maximum entropy principle has a relevant role in image processing, in particular for thresholdin...
The maximum entropy principle is often used for bi-level or multi-level thresholding of images. For ...
In this paper we discuss the formulation, research and development of an optimization process for a ...
We address the problem of image color quantization using a Maximum Entropy based approach. We argue ...
Abstract: The Fisher information (FI) measure is an important concept in statistical estimation theo...
In image processing, the maximum entropy principle is often used for the elaboration of images, in p...
Vol. IInternational audienceThis paper deals with an entropic approach as unsupervised thresholding ...
This dissertation is concerned with an image processing algorithm that performs image enhancement an...
This paper deals with an entropic approach as unsupervised thresholding technique for image processi...
An algorithm for the enhancement of digital images is described. The algorithm is based upon the ana...
Abstract—The global maximum of an entropy function with different decision levels for a three-level ...
This paper compares the effectiveness of the Tsallis entropy over the classic Boltzmann-Gibbs-Shanno...
By combining a maximum conditional entropy principle with a basic equation of (Shannon) information ...
The Boltzmann–Gibbs and Tsallis entropies are essential concepts in statistical physics, which have ...
In image processing, the maximum entropy principle is generally recognized as having a relevant role...
The maximum entropy principle has a relevant role in image processing, in particular for thresholdin...
The maximum entropy principle is often used for bi-level or multi-level thresholding of images. For ...
In this paper we discuss the formulation, research and development of an optimization process for a ...
We address the problem of image color quantization using a Maximum Entropy based approach. We argue ...
Abstract: The Fisher information (FI) measure is an important concept in statistical estimation theo...
In image processing, the maximum entropy principle is often used for the elaboration of images, in p...
Vol. IInternational audienceThis paper deals with an entropic approach as unsupervised thresholding ...
This dissertation is concerned with an image processing algorithm that performs image enhancement an...
This paper deals with an entropic approach as unsupervised thresholding technique for image processi...
An algorithm for the enhancement of digital images is described. The algorithm is based upon the ana...
Abstract—The global maximum of an entropy function with different decision levels for a three-level ...
This paper compares the effectiveness of the Tsallis entropy over the classic Boltzmann-Gibbs-Shanno...
By combining a maximum conditional entropy principle with a basic equation of (Shannon) information ...