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Deep Blind Image Quality Assessment by Learning Sensitivity Map

Citation Author(s):
Submitted by:
Sanghoon Lee
Last updated:
20 April 2018 - 1:12am
Document Type:
Presentation Slides
Document Year:
2018
Event:
Presenters:
Sanghoon Lee
Paper Code:
1426
 

Applying a deep convolutional neural network CNN to no reference image quality assessment (NR-IQA) is a challenging task due to the lack of a training database. In this paper, we propose a CNN-based NR-IQA framework that can effectively solve this problem. The proposed method–the Deep Blind image Quality Assessment predictor (DeepBQA)– adopts two-step training stages to avoid overfitting. In the first stage, a ground-truth objective error map is generated and used as a proxy training target. Then, in the second stage, the subjective score is predicted by learning a sensitivity map, which weights each pixel in the predicted objective error map. To compensate the inaccurate prediction of the objective error on the homogeneous regions, we additionally suggest a reliability map. Experiments showed that DeepBQA yields a state-of-the-art correlation with human opinions.

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