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Learning Deep Vector Regression Model for No-reference Image Quality Assessment
Jie, Gu1,2; Gaofeng, Meng1; Lingfeng, Wang1; Chunhong, Pan1
Conference NameThe 42nd IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
Conference DateMarch 5-9, 2017
Conference PlaceNew Orleans, USA

The goal of no-reference image quality assessment (NR-IQA) is to estimate human perceived image quality without access to either reference image or prior knowledge about distortion type. Previous approaches for this problem are typically based on a regression framework that maps the image features directly to a quality score. In contrast, psychological evidence shows that humans prefer to evaluate visual quality with qualitative descriptions, e.g., using a five-grade ordinal scale: "excellent", "good", "fair", "poor" and "bad". Based on this observation, we propose a vector regression model that predicts five belief scores rather than a single quality score. The belief scores are designed to indicate the confidences of the test image being assigned with these five quality grades. In addition, with the purpose of more extensive applications, a saliency-based pooling strategy is presented to convert the predicted confidences into objective quality scores. Extensive experiments performed on two benchmark datasets demonstrate that our approach achieves state-of-the-art performance and shows great generalization ability.

KeywordImage quality assessment perceptual image quality CNN vector regression saliency-based pooling
Indexed ByEI
Document Type会议论文
Affiliation1.National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences
2.School of Artificial Intelligence, University of Chinese Academy of Sciences
First Author AffilicationChinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100190, Peoples R China
Recommended Citation
GB/T 7714
Jie, Gu,Gaofeng, Meng,Lingfeng, Wang,et al. Learning Deep Vector Regression Model for No-reference Image Quality Assessment[C],2017.
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