
handle: 10281/152014 , 11383/2127894
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.
Image quality assessment, Multidistortion database, No reference metrics, Blur Noise, JPEG, Blur; Image quality assessment; JPEG; Multidistortion database; No reference metrics; Noise;, Image quality assessment Multidistortion database No reference metrics Blur Noise JPEG
Image quality assessment, Multidistortion database, No reference metrics, Blur Noise, JPEG, Blur; Image quality assessment; JPEG; Multidistortion database; No reference metrics; Noise;, Image quality assessment Multidistortion database No reference metrics Blur Noise JPEG
| citations This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | 10 | |
| popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network. | Top 10% | |
| influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | Average | |
| impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network. | Top 10% |
