Energy Loss Estimation Based Reference-Free Quality Assessment of DIBR-Synthesized Views

被引:0
|
作者
Zhang, Huiqing [1 ,2 ,3 ]
Li, Donghao [1 ,3 ]
Xia, Zhifang [1 ,4 ]
Wang, Zichen [1 ]
Wang, Guangchen [5 ]
机构
[1] Beijing Univ Technol, Fac Informat Technol, Beijing 100124, Peoples R China
[2] Minist Educ, Key Lab Artificial Intelligence, Shanghai 200240, Peoples R China
[3] Minist Educ, Engn Res Ctr Digital Community, Beijing 100124, Peoples R China
[4] State Informat Ctr, Beijing, Peoples R China
[5] Wuhan Univ, Natl Engn Res Ctr Multimedia Software, Sch Comp Sci, Wuhan, Peoples R China
关键词
Energy Loss Estimation; Depth Image-Based Rendering; Image Quality Assessment; Reference-Free; IMAGES;
D O I
暂无
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
The processes of warping and rendering in Depth linage-Based Rendering (DIBR) are usually performed without a reference image. Thus, an effective and fast reference-free (RF) image quality assessment (IQA) model devised for DIBR-synthesized views is more favorable. To this aim, this research propose a novel RF IQA method of DIBR-synthesized views based on Energy Loss Estimation. Firstly, considering that the geometric distortions caused by the processes of warping and rendering might increase the high-frequency (HF) information of a DIBR-synthesized view, the influence of the geometric distortion on the visual quality of a synthesized view can be effectively evaluated by estimating the HF information energies of a given DIBR-synthesized view in the log-gabor domain. Secondly. we estimate the energy loss in the process of JPEG compression as the image complexity, which is used to overcome the problem of different amounts of HF information contained in different content images. Finally, the proposed RF DIBR-synthesized IQA metric is obtained by using the energy loss in JPEG compression to normalize the HF information energy in the log-gabor domain. Experiments conducted on two public datasets demonstrate that the proposed method is advantageous over the relevant state-of-the-art RF IQA metrics.
引用
收藏
页码:3098 / 3103
页数:6
相关论文
共 50 条
  • [41] Merqury: reference-free quality, completeness, and phasing assessment for genome assemblies
    Arang Rhie
    Brian P. Walenz
    Sergey Koren
    Adam M. Phillippy
    [J]. Genome Biology, 21
  • [42] Merqury: reference-free quality, completeness, and phasing assessment for genome assemblies
    Rhie, Arang
    Walenz, Brian P.
    Koren, Sergey
    Phillippy, Adam M.
    [J]. GENOME BIOLOGY, 2020, 21 (01)
  • [43] A reference-free underwater image quality assessment metric in frequency domain
    Yang, Ning
    Zhong, Qihang
    Li, Kun
    Cong, Runmin
    Zhao, Yao
    Kwong, Sam
    [J]. SIGNAL PROCESSING-IMAGE COMMUNICATION, 2021, 94
  • [44] TransRate: reference-free quality assessment of de novo transcriptome assemblies
    Smith-Unna, Richard
    Boursnell, Chris
    Patro, Rob
    Hibberd, Julian M.
    Kelly, Steven
    [J]. GENOME RESEARCH, 2016, 26 (08) : 1134 - 1144
  • [45] A NEW REFERENCE-FREE IMAGE QUALITY INDEX FOR BLUR ESTIMATION IN THE FREQUENCY DOMAIN
    Chetouani, Aladine
    Beghdadi, Azeddine
    Deriche, Mohamed
    [J]. 2009 IEEE INTERNATIONAL SYMPOSIUM ON SIGNAL PROCESSING AND INFORMATION TECHNOLOGY (ISSPIT 2009), 2009, : 155 - +
  • [46] Reference-Free Quality Assessment of Sonar Images via Contour Degradation Measurement
    Chen, Weiling
    Gu, Ke
    Lin, Weisi
    Xia, Zhifang
    Le Callet, Patrick
    Cheng, En
    [J]. IEEE TRANSACTIONS ON IMAGE PROCESSING, 2019, 28 (11) : 5336 - 5351
  • [47] A machine-learning framework for automatic reference-free quality assessment in MRI
    Kuestner, T.
    Gatidis, S.
    Liebgott, A.
    Schwartz, M.
    Mauch, L.
    Martirosian, P.
    Schmidt, H.
    Schwenzer, N. F.
    Nikolaou, K.
    Bamberg, F.
    Yang, B.
    Schick, F.
    [J]. MAGNETIC RESONANCE IMAGING, 2018, 53 : 134 - 147
  • [48] NIQSV: A NO REFERENCE IMAGE QUALITY ASSESSMENT METRIC FOR 3D SYNTHESIZED VIEWS
    Tian, Shishun
    Zhang, Lu
    Morin, Luce
    Deforges, Olivier
    [J]. 2017 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH AND SIGNAL PROCESSING (ICASSP), 2017, : 1248 - 1252
  • [49] Learning-Based Reference-Free Speech Quality Measures for Hearing Aid Applications
    Salehi, Haniyeh
    Suelzle, David
    Folkeard, Paula
    Parsa, Vijay
    [J]. IEEE-ACM TRANSACTIONS ON AUDIO SPEECH AND LANGUAGE PROCESSING, 2018, 26 (12) : 2277 - 2288
  • [50] Deep Learning to Automate Reference-Free Image Quality Assessment of Whole-Heart MR Images
    Piccini, Davide
    Demesmaeker, Robin
    Heerfordt, John
    Yerly, Jerome
    Di Sopra, Lorenzo
    Masci, Pier Giorgio
    Schwitter, Juerg
    Van De Ville, Dimitri
    Richiardi, Jonas
    Kober, Tobias
    Stuber, Matthias
    [J]. RADIOLOGY-ARTIFICIAL INTELLIGENCE, 2020, 2 (03)