You have requested a machine translation of selected content from our databases. This functionality is provided solely for your convenience and is in no way intended to replace human translation. Neither SPIE nor the owners and publishers of the content make, and they explicitly disclaim, any express or implied representations or warranties of any kind, including, without limitation, representations and warranties as to the functionality of the translation feature or the accuracy or completeness of the translations.
Translations are not retained in our system. Your use of this feature and the translations is subject to all use restrictions contained in the Terms and Conditions of Use of the SPIE website.
3 September 2008Image analysis through high-order entropy measures extracted from time-frequency representations
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.
Salvador Gabarda andGabriel 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
The alert did not successfully save. Please try again later.
Salvador Gabarda, 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