Le Moan, Steven ; Urban, Philipp (2014)
Image-Difference Prediction: From Color to Spectral.
In: IEEE Transactions on Image Processing, 23 (5)
doi: 10.1109/TIP.2014.2311373
Artikel, Bibliographie
Kurzbeschreibung (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.
| Typ des Eintrags: | Artikel |
|---|---|
| Erschienen: | 2014 |
| Autor(en): | Le Moan, Steven ; Urban, Philipp |
| Art des Eintrags: | Bibliographie |
| Titel: | Image-Difference Prediction: From Color to Spectral |
| Sprache: | Englisch |
| Publikationsjahr: | 2014 |
| Titel der Zeitschrift, Zeitung oder Schriftenreihe: | IEEE Transactions on Image Processing |
| Jahrgang/Volume einer Zeitschrift: | 23 |
| (Heft-)Nummer: | 5 |
| DOI: | 10.1109/TIP.2014.2311373 |
| Kurzbeschreibung (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. |
| Freie Schlagworte: | Business Field: Visual decision support, Research Area: Human computer interaction (HCI), Research Area: Modeling (MOD), Image quality, Color analysis, Color perception, Multispectral images |
| Fachbereich(e)/-gebiet(e): | 20 Fachbereich Informatik 20 Fachbereich Informatik > Graphisch-Interaktive Systeme |
| Hinterlegungsdatum: | 12 Nov 2018 11:16 |
| Letzte Änderung: | 12 Nov 2018 11:16 |
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