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A NOVEL BLIND IMAGE QUALITY ASSESSMENT METHOD BASED ON REFINED NATURAL SCENE STATISTICS

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Citation Author(s):
Fu-Zhao Ou, Yuan-Gen Wang, Guopu Zhu
Submitted by:
Fuzhao Ou
Last updated:
20 September 2019 - 9:56am
Document Type:
Poster
Document Year:
2019
Event:
Presenters Name:
Fu-Zhao Ou
Paper Code:
1552

Abstract 

Abstract: 

Natural scene statistics (NSS) model has received considerable attention in the image quality assessment (IQA) community due to its high sensitivity to image distortion. However, most existing NSS-based IQA methods extract features either from spatial domain or from transform domain. There is little work to simultaneously consider the features from these two domains. In this paper, a novel blind IQA method (NBIQA) based on refined NSS is proposed. The proposed NBIQA first investigates the performance of a large number of candidate features from both the spatial and transform domains. Based on the investigation, we construct a refined NSS model by selecting competitive features from existing NSS models and adding three new features. Then the refined NSS is fed into SVM tool to learn a simple regression model. Finally, the trained regression model is used to predict the scalar quality score of the image. Experimental results tested on both LIVE IQA and LIVE-C databases show that the proposed NBIQA performs better in terms of synthetic and authentic image distortion than current mainstream IQA methods.

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