Joint binocular energy-contrast perception for quality assessment of stereoscopic images

被引:14
|
作者
Ma, Jian
An, Ping [1 ]
Shen, Liquan
Li, Kai
机构
[1] Shanghai Univ, Sch Commun & Informat Engn, Shanghai Inst Adv Commun & Data Sci, Shanghai 200444, Peoples R China
基金
中国国家自然科学基金;
关键词
Binocular visual system; Stereoscopic image quality; Full reference; CSF; Binocular energy-contrast perception; HORIZONTAL DISPARITY; VIDEO; MODELS; RESPONSES;
D O I
10.1016/j.image.2018.03.009
中图分类号
TM [电工技术]; TN [电子技术、通信技术];
学科分类号
0808 ; 0809 ;
摘要
Binocular visual system (BVS) can perceive the difference between left and right retinal images to create a mental image with depth perception, which is in consequence of two binocular interactions, i.e., binocular fusion and rivalry. To study the effective method of accounting for binocular fusion and rivalry in stereoscopic image quality assessment (SIQA) design, in this paper, a novel full reference (FR) SIQA metric is proposed by jointly considering binocular energy-contrast perception (BECP). As a major technical contribution, we design a dual-channel model for SIQA that more effectively mimic binocular fusion and rivalry mechanisms of the BVS. Specifically, since the binocular visual sensitivity of stimulus at different spatial frequencies is different, each image of the reference and distorted stereopairs is first filtered independently by a contrast sensitivity function (CSF). Constructively, the weights of relative contribution of each view for binocular fusion are calculated based on a magnitude response of Log-Gabor filtering measure. Further, the weights of relative contribution of each view for dominant perception are calculated by utilizing a block-based contrast measure. Finally, the overall perceived quality of a stereoscopic image is obtained by the quality scores combining of the BECP. Experiments are performed on publicly available symmetric and asymmetric subjected stereoscopic image databases, which demonstrate that the proposed metric achieves high consistency with human opinions and significantly higher prediction accuracy than the state-of-the-art FR-SIQA methods.
引用
收藏
页码:33 / 45
页数:13
相关论文
共 50 条
  • [41] Stereoscopic Image Quality Assessment by Cons ring Binocular Visual Mechanisms
    Sun, Guangming
    Ding, Yong
    Deng, Ruizhe
    Zhao, Yang
    Chen, Xiaodong
    Krylov, Andrey S.
    IEEE ACCESS, 2018, 6 : 51337 - 51347
  • [42] No-reference stereoscopic image quality assessment based on binocular collaboration
    Wang, Hanling
    Ke, Xiao
    Guo, Wenzhong
    Zheng, Wukun
    Neural Networks, 2024, 180
  • [43] Stereoscopic image quality assessment combining statistical features and binocular theory
    Yang, Jiachen
    Xu, Huifang
    Zhao, Yang
    Liu, Hehan
    Lu, Wen
    PATTERN RECOGNITION LETTERS, 2019, 127 : 48 - 55
  • [44] No-reference stereoscopic images quality assessment method based on monocular superpixel visual features and binocular visual features
    Zheng, Zhi
    Liu, Yun
    Liu, Yun
    Huang, Baoqing
    Yu, Hongwei
    JOURNAL OF VISUAL COMMUNICATION AND IMAGE REPRESENTATION, 2020, 71
  • [45] Quality Assessment For Stereoscopic Images With JPEG Compression Errors
    Voo, Kenny H. B.
    Bong, David B. L.
    2015 IEEE INTERNATIONAL CONFERENCE ON CONSUMER ELECTRONICS - TAIWAN (ICCE-TW), 2015, : 220 - 221
  • [46] Stereoscopic Images Quality Assessment Based On Deep Learning
    Wang, Kai
    Zhou, Jun
    Liu, Ning
    Gu, Xiao
    2016 30TH ANNIVERSARY OF VISUAL COMMUNICATION AND IMAGE PROCESSING (VCIP), 2016,
  • [47] UNIVERSAL BLIND IMAGE QUALITY ASSESSMENT FOR STEREOSCOPIC IMAGES
    Fezza, Sid Ahmed
    Chetouani, Aladine
    Larabi, Mohamed-Chaker
    2016 3DTV-CONFERENCE: THE TRUE VISION - CAPTURE, TRANSMISSION AND DISPLAY OF 3D VIDEO (3DTV-CON), 2016,
  • [48] Perceptual objective quality assessment of stereoscopic stitched images
    Yan, Weiqing
    Yue, Guanghui
    Fang, Yuming
    Chen, Hua
    Tang, Chang
    Jiang, Gangyi
    SIGNAL PROCESSING, 2020, 172
  • [49] No Reference Quality Assessment for Stereoscopic Images by Statistical Features
    Fang, Yuming
    Yan, Jiebin
    Wang, Jiheng
    2017 NINTH INTERNATIONAL CONFERENCE ON QUALITY OF MULTIMEDIA EXPERIENCE (QOMEX), 2017,
  • [50] Toward a Blind Deep Quality Evaluator for Stereoscopic Images Based on Monocular and Binocular Interactions
    Shao, Feng
    Tian, Weijun
    Lin, Weisi
    Jiang, Gangyi
    Dai, Qionghai
    IEEE TRANSACTIONS ON IMAGE PROCESSING, 2016, 25 (05) : 2059 - 2074