Image database TID2013: Peculiarities, results and perspectives
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Image database TID2013 : Peculiarities, results and perspectives. / Ponomarenko, Nikolay; Jin, Lina; Ieremeiev, Oleg; Lukin, Vladimir; Egiazarian, Karen; Astola, Jaakko; Vozel, Benoit; Chehdi, Kacem; Carli, Marco; Battisti, Federica; Jay Kuo, C. C.
In: Signal Processing: Image Communication, Vol. 30, 01.01.2015, p. 57-77.Research output: Contribution to journal › Article › Scientific › peer-review
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TY - JOUR
T1 - Image database TID2013
T2 - Peculiarities, results and perspectives
AU - Ponomarenko, Nikolay
AU - Jin, Lina
AU - Ieremeiev, Oleg
AU - Lukin, Vladimir
AU - Egiazarian, Karen
AU - Astola, Jaakko
AU - Vozel, Benoit
AU - Chehdi, Kacem
AU - Carli, Marco
AU - Battisti, Federica
AU - Jay Kuo, C. C.
PY - 2015/1/1
Y1 - 2015/1/1
N2 - 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.
AB - 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.
KW - Image denoising
KW - Image lossy compression
KW - Image visual quality metrics
UR - http://www.scopus.com/inward/record.url?scp=84919839405&partnerID=8YFLogxK
U2 - 10.1016/j.image.2014.10.009
DO - 10.1016/j.image.2014.10.009
M3 - Article
VL - 30
SP - 57
EP - 77
JO - Signal Processing: Image Communication
JF - Signal Processing: Image Communication
SN - 0923-5965
ER -