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3 September 2008 Image analysis through high-order entropy measures extracted from time-frequency representations
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Abstract
Entropy is used as a number indicating the amount of uncertainty or information of a source. That means that noise can not be distinguished from information by simply measuring entropy. Nevertheless, the Renyi entropy can be used to calculate the entropy in a pixel-wise basis. When the source of information is a digital image, a value of entropy can be assigned to each pixel of the image. Consequently, entropy histograms of images can be obtained. Entropy histograms give information about the image information contents in a similar way as image histograms give information about the distribution of gray-levels. Hence, histograms of entropy can be used to quantify differences in the information contents of images. The pixel-wise entropy of digital images has been calculated through the use of a spatial/spatial-frequency distribution. The generalized Renyi entropy and a normalized windowed pseudo-Wigner distribution (PWD) have been selected to obtain particular pixel-wise entropy values. In this way, a histogram of entropy values has been derived. In this paper, first we present a review on the use of the Renyi entropy as a measure of the information contents extracted from a time-frequency representation. Second, a particular measure based on a high-order Renyi entropy distribution has been analyzed. Examples are presented in the areas of image fusion and blind image quality assessment. Experiments on real data in different applications domains illustrate the robustness and utilization of this method.
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Salvador Gabarda and Gabriel Cristóbal "Image analysis through high-order entropy measures extracted from time-frequency representations", Proc. SPIE 7074, Advanced Signal Processing Algorithms, Architectures, and Implementations XVIII, 70740Z (3 September 2008); https://doi.org/10.1117/12.797300
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