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Deep Blind Image Quality Assessment by Learning Sensitivity Map

Abstract: 

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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Paper Details

Authors:
Submitted On:
20 April 2018 - 1:12am
Short Link:
Type:
Presentation Slides
Event:
Presenter's Name:
Sanghoon Lee
Paper Code:
1426
Document Year:
2018
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Document Files

ICASSP2018_Sanghoon_Lee.pdf

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[1] , "Deep Blind Image Quality Assessment by Learning Sensitivity Map", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3074. Accessed: Sep. 24, 2018.
@article{3074-18,
url = {http://sigport.org/3074},
author = { },
publisher = {IEEE SigPort},
title = {Deep Blind Image Quality Assessment by Learning Sensitivity Map},
year = {2018} }
TY - EJOUR
T1 - Deep Blind Image Quality Assessment by Learning Sensitivity Map
AU -
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3074
ER -
. (2018). Deep Blind Image Quality Assessment by Learning Sensitivity Map. IEEE SigPort. http://sigport.org/3074
, 2018. Deep Blind Image Quality Assessment by Learning Sensitivity Map. Available at: http://sigport.org/3074.
. (2018). "Deep Blind Image Quality Assessment by Learning Sensitivity Map." Web.
1. . Deep Blind Image Quality Assessment by Learning Sensitivity Map [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3074