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Quality Assessment for CRT and LCD Color Reproduction Using a Blind Metric
Bringier, B. (Universite de Poitiers)
Quintard, L. (Universite de Poitiers)
Larabi, M.-C. (Laboratoire National de metrologie et d’Essais (Trappes, França))

Data: 2008
Resum: This paper deals with image quality assessment that is capturing the focus of several research teams from academic and industrial parts. This field has an important role in various applications related to image from acquisition to projection. A large numbers of objective image quality metrics have been developed during the last decade. These metrics are more or less correlated to end-user feedback and can be separated in three categories: 1) Full Reference (FR) trying to evaluate the impairment in comparison to the reference image, 2) Reduced Reference (RR) using some features extracted from an image to represent it and compare it with the distorted one and 3) No Reference (NR) measures known as distortions such as blockiness, blurriness,. . . without the use of a reference. Unfortunately, the quality assessment community have not achieved a universal image quality model and only empiricalmodels established on psychophysical experimentation are generally used. In this paper, we focus only on the third category to evaluate the quality of CRT (Cathode Ray Tube) and LCD (Liquid Crystal Display) color reproduction where a blind metric is, based on modeling a part of the human visual system behavior. The objective results are validated by single-media and cross-media subjective tests. This allows to study the ability of simulating displays on a reference one.
Drets: Aquest document està subjecte a una llicència d'ús Creative Commons. Es permet la reproducció total o parcial i la comunicació pública de l'obra, sempre que no sigui amb finalitats comercials, i sempre que es reconegui l'autoria de l'obra original. No es permet la creació d'obres derivades. Creative Commons
Llengua: Anglès.
Document: article ; recerca ; publishedVersion
Publicat a: ELCVIA : Electronic Letters on Computer Vision and Image Analysis, V. 7 n. 3 (2008) p. 23-35, ISSN 1577-5097

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DOI: 10.5565/rev/elcvia.181

13 p, 420.3 KB

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