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Image-Difference Prediction: From Color to Spectral

Le Moan, Steven and Urban, Philipp (2014):
Image-Difference Prediction: From Color to Spectral.
23, In: IEEE Transactions on Image Processing, (5), pp. 2058-2068, DOI: 10.1109/TIP.2014.2311373,
[Article]

Abstract

We propose a new strategy to evaluate the quality of multi and hyperspectral images, from the perspective of human perception. We define the spectral image difference as the overall perceived difference between two spectral images under a set of specified viewing conditions (illuminants). First, we analyze the stability of seven image-difference features across illuminants, by means of an information-theoretic strategy. We demonstrate, in particular, that in the case of common spectral distortions (spectral gamut mapping, spectral compression, spectral reconstruction), chromatic features vary much more than achromatic ones despite considering chromatic adaptation. Then, we propose two computationally efficient spectral image difference metrics and compare them to the results of a subjective visual experiment. A significant improvement is shown over existing metrics such as the widely used root-mean square error.

Item Type: Article
Erschienen: 2014
Creators: Le Moan, Steven and Urban, Philipp
Title: Image-Difference Prediction: From Color to Spectral
Language: English
Abstract:

We propose a new strategy to evaluate the quality of multi and hyperspectral images, from the perspective of human perception. We define the spectral image difference as the overall perceived difference between two spectral images under a set of specified viewing conditions (illuminants). First, we analyze the stability of seven image-difference features across illuminants, by means of an information-theoretic strategy. We demonstrate, in particular, that in the case of common spectral distortions (spectral gamut mapping, spectral compression, spectral reconstruction), chromatic features vary much more than achromatic ones despite considering chromatic adaptation. Then, we propose two computationally efficient spectral image difference metrics and compare them to the results of a subjective visual experiment. A significant improvement is shown over existing metrics such as the widely used root-mean square error.

Journal or Publication Title: IEEE Transactions on Image Processing
Volume: 23
Number: 5
Uncontrolled Keywords: Business Field: Visual decision support, Research Area: Human computer interaction (HCI), Research Area: Modeling (MOD), Image quality, Color analysis, Color perception, Multispectral images
Divisions: 20 Department of Computer Science
20 Department of Computer Science > Interactive Graphics Systems
Date Deposited: 12 Nov 2018 11:16
DOI: 10.1109/TIP.2014.2311373
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