CNN Based No-Reference HDR Image Quality Assessment  

作　　者：FAN Kefeng LIANG Jiyun LI Fei QIU Puye 

机构地区：[1]Digital Technology Research Center,China Electronics Standardization Institute,Beijing 100007,China [2]School of Electronic Engineering and Automation,Guilin University of Electronic Technology,Guilin 541004,China [3]Guangzhou SequoiaDB Co.,Guangzhou 510006,China

出　　处：《Chinese Journal of Electronics》2021年第2期282-288,共7页电子学报（英文版）

基　　金：support by The National Key Research and Development Program of China(No.2019YFB1405503);2019 Public Service Platform of Industrial Technology Foundation of MIIT(No.2019-00895-2-1)。

摘　　要：Motivated by the problems of non-universality and over-reliance on the original reference image in High dynamic range(HDR)Image quality assessment(IQA),a convolutional neural network-based algorithm for no-reference HDR image quality assessment is proposed.The Salience detection by self-resemblance(SDSR)algorithm which extracts the salient regions of the HDR image,is used to simulate the human visual attention mechanism.Then a visual quality perception network for training quality prediction models is designed according to the visual characteristics of luminance and contrast sensitivity.And this network consists of an Error estimation network(Error-net),a Perceptual resistance network(PR-net)and a mixing function.The experimental results indicate that the method proposed has high consistency with subjective perception,and the value of assessment metrics Spearman rank-order correlation coefficient(SROCC),Pearson product-moment correlation coefficient(PLCC)and Root mean square error(RMSE)correspondingly reaches 0.941,0.910 and 8.176 as well.It is comparable with classic full-reference HDR IQA methods.

关 键 词：High dynamic range image Image quality assessment Convolutional neural network Human visual system 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术]