A Complexity-Based Image Analysis to Investigate Interference Between Distortions and Image Contents in Image Quality Assessment

被引：1
|
作者：
Ciocca, Gianluigi [1 ,2 ]
Corchs, Silvia [1 ,2 ]
Gasparini, Francesca [1 ,2 ]
机构：
[1] Univ Milano Bicocca, Dipartimento Informat Sistemist & Comunicaz, Viale Sarca 336, I-20126 Milan, Italy
[2] NeuroMi Milan Ctr Neurosci, Milan, Italy
来源：
COMPUTATIONAL COLOR IMAGING, CCIW 2017 | 2017年 / 10213卷
关键词：
Image complexity; Image Quality Assessment; Full Reference metrics; TO-NOISE RATIO; ASSESSMENT ALGORITHMS; VISUAL QUALITY; INFORMATION; SIMILARITY; JPEG2000; INDEX;
D O I：
10.1007/978-3-319-56010-6_9
中图分类号：
TP301 [理论、方法];
学科分类号：
081202 ;
摘要：
In this paper we investigate how distortion and image content interfere within image quality assessment. To this end we analyze how full reference metrics behave within three different groups of images. Given a dataset of images, these are first classified as high, medium or low complexity and the FR methods are applied within each group separately. We consider images from LIVE, CSIQ and LIVE multi-distorted databases. We evaluate 17 full reference quality metrics available in the literature on each of these the high, medium and low complexity groups. We observe that within these groups the metrics better correlate subjective data. In particular, the signal based metrics are the ones that show the highest improvements. Moreover for the LIVE multi-distorted database the gain in performance is evident for all the metrics considered.
引用
收藏
页码：105 / 121
页数：17
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