No-Reference Image Quality Assessment and Application Based on Spatial Domain Coding

被引:6
|
作者
Chen Yong [1 ]
Fang Hao [1 ]
Liu Huanlin [2 ]
机构
[1] Chongqing Univ Posts & Telecommun, Key Lab Ind Internet Things & Network Control, Minist Educ, Chongqing 400065, Peoples R China
[2] Chongqing Univ Posts & Telecommun, Sch Telecommun & Informat Engn, Chongqing 400065, Peoples R China
来源
IEEE ACCESS | 2018年 / 6卷
关键词
Local binary patterns; no-reference image quality assessment; neural network; INFORMATION; STATISTICS;
D O I
10.1109/ACCESS.2018.2875951
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
Aiming at the problem that a no-reference (NR) image quality assessment algorithm is not accurate enough to predict the image quality at present, we proposed a method of NR image quality assessment based on spatial domain coding (SDC). We extracted the spatial structure features of the image on different bit planes. The extracted features quantify the structural information between pixels, which can more accurately reflect the distortion degree of the image. We used a neural network to establish an image quality assessment model. The experimental results show that the proposed image assessment algorithm is more accurate than the existing mainstream image quality assessment algorithms and highly consistent with subjective perception of human eyes. Finally, the proposed algorithm is applied to the auto focus of the camera verifying the practicability and accuracy of the algorithm.
引用
收藏
页码:60456 / 60466
页数:11
相关论文
共 50 条
  • [21] No-reference image quality assessment based on sparse representation
    Xichen Yang
    Quansen Sun
    Tianshu Wang
    Neural Computing and Applications, 2019, 31 : 6643 - 6658
  • [22] No-reference image quality assessment based on sparse representation
    Yang, Xichen
    Sun, Quansen
    Wang, Tianshu
    NEURAL COMPUTING & APPLICATIONS, 2019, 31 (10): : 6643 - 6658
  • [23] CNN Based No-Reference HDR Image Quality Assessment
    Kefeng, Fan
    Jiyun, Liang
    Fei, Li
    Puye, Qiu
    CHINESE JOURNAL OF ELECTRONICS, 2021, 30 (02) : 282 - 288
  • [24] A no-reference image blurriness metric in the spatial domain
    Hong, Yuzhen
    Ren, Guoqiang
    Liu, Enhai
    OPTIK, 2016, 127 (14): : 5568 - 5575
  • [25] No-reference image quality assessment based on global awareness
    Hu, Zhigang
    Yang, Gege
    Du, Zhe
    Huang, Xiaodong
    Zhang, Pujing
    Liu, Dechun
    PLOS ONE, 2024, 19 (10):
  • [26] An improved model for no-reference image quality assessment and a no-reference video quality assessment model based on frame analysis
    Mukesh Kumar Rohil
    Neetika Gupta
    Prakash Yadav
    Signal, Image and Video Processing, 2020, 14 : 205 - 213
  • [27] No-reference Image Quality Assessment Based on Differential Excitation
    Chen Y.
    Wu M.-M.
    Fang H.
    Liu H.-L.
    Zidonghua Xuebao/Acta Automatica Sinica, 2020, 46 (08): : 1727 - 1737
  • [28] NO-REFERENCE IMAGE QUALITY ASSESSMENT BASED ON VISUAL CODEBOOK
    Ye, Peng
    Doermann, David
    2011 18TH IEEE INTERNATIONAL CONFERENCE ON IMAGE PROCESSING (ICIP), 2011,
  • [29] No-reference image quality assessment based on hybrid model
    Li, Jie
    Yan, Jia
    Deng, Dexiang
    Shi, Wenxuan
    Deng, Songfeng
    SIGNAL IMAGE AND VIDEO PROCESSING, 2017, 11 (06) : 985 - 992
  • [30] CNN Based No-Reference HDR Image Quality Assessment
    FAN Kefeng
    LIANG Jiyun
    LI Fei
    QIU Puye
    ChineseJournalofElectronics, 2021, 30 (02) : 282 - 288