In this paper we introduce a multidistortion database, where 10 pristine color images have been simultaneously distorted by two types of distortions: blur and JPEG and noise and JPEG. The two datasets consist of respectively 350 and 400 images, and have been subjectively evaluated within two psycho-physical experiments. We here also propose two no reference multidistortion metrics, one for each of the two datasets, as linear combinations of no reference single distortion ones. The optimized weights of the combinations are obtained using particle swarm optimization. The different combinations proposed show good performance when correlated with the subjective scores of the multidistortion database.
Corchs, S., Gasparini, F. (2017). A multidistortion database for image quality. Intervento presentato a: Computational Color Imaging. CCIW 2017, Milano [10.1007/978-3-319-56010-6_8].
A multidistortion database for image quality
CORCHS, SILVIA ELENA
Primo
;GASPARINI, FRANCESCAUltimo
2017
Abstract
In this paper we introduce a multidistortion database, where 10 pristine color images have been simultaneously distorted by two types of distortions: blur and JPEG and noise and JPEG. The two datasets consist of respectively 350 and 400 images, and have been subjectively evaluated within two psycho-physical experiments. We here also propose two no reference multidistortion metrics, one for each of the two datasets, as linear combinations of no reference single distortion ones. The optimized weights of the combinations are obtained using particle swarm optimization. The different combinations proposed show good performance when correlated with the subjective scores of the multidistortion database.
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