Image database TID2013: Peculiarities, results and perspectives

Abstract : This paper describes a recently created image database, TID2013, intended for evaluation of full-reference visual quality assessment metrics. With respect to TID2008, the new database contains a larger number (3000) of test images obtained from 25 reference images, 24 types of distortions for each reference image, and 5 levels for each type of distortion. Motivations for introducing 7 new types of distortions and one additional level of distortions are given; examples of distorted images are presented. Mean opinion scores (MOS) for the new database have been collected by performing 985 subjective experiments with volunteers (observers) from five countries (Finland, France, Italy, Ukraine, and USA). The availability of MOS allows the use of the designed database as a fundamental tool for assessing the effectiveness of visual quality. Furthermore, existing visual quality metrics have been tested with the proposed database and the collected results have been analyzed using rank order correlation coefficients between MOS and considered metrics. These correlation indices have been obtained both considering the full set of distorted images and specific image subsets, for highlighting advantages and drawbacks of existing, state of the art, quality metrics. Approaches to thorough performance analysis for a given metric are presented to detect practical situations or distortion types for which this metric is not adequate enough to human perception. The created image database and the collected MOS values are freely available for downloading and utilization for scientific purposes.
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Article dans une revue
Signal Processing: Image Communication, Elsevier, 2015, 30, pp.57-77. 〈10.1016/j.image.2014.10.009〉
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Soumis le : lundi 29 juin 2015 - 15:40:34
Dernière modification le : jeudi 11 janvier 2018 - 06:24:21
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Nikolay Ponomarenko, Lina Jin, Oleg Ieremeiev, Vladimir Lukin, Karen Egiazarian, et al.. Image database TID2013: Peculiarities, results and perspectives. Signal Processing: Image Communication, Elsevier, 2015, 30, pp.57-77. 〈10.1016/j.image.2014.10.009〉. 〈hal-01109219〉



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