|Table of Contents|

No-reference Image Quality Assessment Based on DeepNetwork and Visual Characteristics(PDF)

《南京师大学报(自然科学版)》[ISSN:1001-4616/CN:32-1239/N]

Issue:
2019年03期
Page:
20-26
Research Field:
·全国机器学习会议论文专栏·
Publishing date:

Info

Title:
No-reference Image Quality Assessment Based on DeepNetwork and Visual Characteristics
Author(s):
Gao Fangyuan1He Lihuo2
1.School of Mathematics and Systems Science,Beihang University,Beijing 102206,China)(2.School of Electronic Engeering,Xidian University,Xi’an 710071,China
Keywords:
image quality assessmentdeep networkvisual characteristicworst-case pooling mechanism
PACS:
TP391
DOI:
10.3969/j.issn.1001-4616.2019.03.003
Abstract:
Image quality assessment(IQA)is a fundamental problem in image processing and computer vision. IQA is very important for the performance evaluation of acquisition,processing and analysis systems of visual information. Most of the existing no-reference(NR)image quality assessments are based on the natural statistical characteristics,or design deep network to predict the image quality. These methods have not considered the visual characteristics,which produces the large difference in image quality score between the objective and subjective methods. To overcome this problem,this paper proposes a NR-IQA method based on the deep network and visual characteristics. Firstly,mean subtracted contrast normalized map of the image is calculated,and the map is resampled into overlapped patches randomly. Then the deep network is designed by combining the VGG net and Inception net to predict the quality scores of patches. Finally,the scores are ranked and the worst-case pooling is utilized to weight the scores to obtain the image quality score. Experimental results on public CSIQ,LIVE and TID2013 databases show that the proposed method performs consistently with the subjective perception and has a better performance than state-of-the-art methods.

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Last Update: 2019-09-30