SWVFS: a saliency weighted visual feature similarity metric for image quality assessment

2013 ◽  
Vol 8 (1) ◽  
pp. 145-155 ◽  
Author(s):  
Li Cui
Keyword(s):  
Image Quality ◽  
Quality Assessment ◽  
Image Quality Assessment ◽  
Visual Feature ◽  
Similarity Metric ◽  
Feature Similarity

PMFS: A Perceptual Modulated Feature Similarity Metric for Stereoscopic Image Quality Assessment

2014 ◽  
Vol 21 (8) ◽  
pp. 1003-1006 ◽  
Author(s):  
Wujie Zhou ◽  
Gangyi Jiang ◽  
Mei Yu ◽  
Feng Shao ◽  
Zongju Peng
Keyword(s):  
Image Quality ◽  
Quality Assessment ◽  
Image Quality Assessment ◽  
Stereoscopic Image ◽  
Similarity Metric ◽  
Feature Similarity

scholarly journals FSIM: A Feature Similarity Index for Image Quality Assessment

2011 ◽  
Vol 20 (8) ◽  
pp. 2378-2386 ◽  
Author(s):  
Lin Zhang ◽  
Lei Zhang ◽  
Xuanqin Mou ◽  
D. Zhang
Keyword(s):  
Image Quality ◽  
Quality Assessment ◽  
Image Quality Assessment ◽  
Similarity Index ◽  
Feature Similarity ◽  
Feature Similarity Index

scholarly journals Cross-Domain Feature Similarity Guided Blind Image Quality Assessment

2022 ◽  
Vol 15 ◽  
Author(s):  
Chenxi Feng ◽  
Long Ye ◽  
Qin Zhang
Keyword(s):  
Image Quality ◽  
Quality Assessment ◽  
Image Quality Assessment ◽  
Perceptual Quality ◽  
Synthetic Image ◽  
Quality Prediction ◽  
Cross Domain ◽  
Blind Image Quality Assessment ◽  
Feature Similarity ◽  
Blind Image

This work proposes an end-to-end cross-domain feature similarity guided deep neural network for perceptual quality assessment. Our proposed blind image quality assessment approach is based on the observation that features similarity across different domains (e.g., Semantic Recognition and Quality Prediction) is well correlated with the subjective quality annotations. Such phenomenon is validated by thoroughly analyze the intrinsic interaction between an object recognition task and a quality prediction task in terms of characteristics of the human visual system. Based on the observation, we designed an explicable and self-contained cross-domain feature similarity guided BIQA framework. Experimental results on both authentical and synthetic image quality databases demonstrate the superiority of our approach, as compared to the state-of-the-art models.

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