No-Reference Quality Assessment of Transmitted Stereoscopic Videos Based on Human Visual System

被引:2
|
作者
Hasan, Md Mehedi [1 ]
Islam, Md Ariful [1 ]
Rahman, Sejuti [1 ]
Frater, Michael R. [2 ]
Arnold, John F. [2 ]
机构
[1] Univ Dhaka, Dept Robot & Mechatron Engn, Dhaka 1000, Bangladesh
[2] Univ New South Wales, Sch Engn & Informat Technol, Canberra, ACT 2600, Australia
来源
APPLIED SCIENCES-BASEL | 2022年 / 12卷 / 19期
关键词
quality assessment; stereoscopic video; disparity index; human visual system; no-reference; BINOCULAR-RIVALRY; EXPERIENCE; TRANSMISSION; COMPRESSION;
D O I
10.3390/app121910090
中图分类号
O6 [化学];
学科分类号
0703 ;
摘要
Provisioning the stereoscopic 3D (S3D) video transmission services of admissible quality in a wireless environment is an immense challenge for video service providers. Unlike for 2D videos, a widely accepted No-reference objective model for assessing transmitted 3D videos that explores the Human Visual System (HVS) appropriately has not been developed yet. Distortions perceived in 2D and 3D videos are significantly different due to the sophisticated manner in which the HVS handles the dissimilarities between the two different views. In real-time video transmission, viewers only have the distorted or receiver end content of the original video acquired through the communication medium. In this paper, we propose a No-reference quality assessment method that can estimate the quality of a stereoscopic 3D video based on HVS. By evaluating perceptual aspects and correlations of visual binocular impacts in a stereoscopic movie, the approach creates a way for the objective quality measure to assess impairments similarly to a human observer who would experience the similar material. Firstly, the disparity is measured and quantified by the region-based similarity matching algorithm, and then, the magnitude of the edge difference is calculated to delimit the visually perceptible areas of an image. Finally, an objective metric is approximated by extracting these significant perceptual image features. Experimental analysis with standard S3D video datasets demonstrates the lower computational complexity for the video decoder and comparison with the state-of-the-art algorithms shows the efficiency of the proposed approach for 3D video transmission at different quantization (QP 26 and QP 32) and loss rate (1% and 3% packet loss) parameters along with the perceptual distortion features.
引用
收藏
页数:18
相关论文
共 50 条
  • [31] A NO-REFERENCE QUALITY ASSESSMENT METHOD FOR DIGITAL HUMAN HEAD
    Zhou, Yingjie
    Zhang, Zicheng
    Sun, Wei
    Min, Xiongkuo
    Ma, Xianghe
    Zhai, Guangtao
    2023 IEEE INTERNATIONAL CONFERENCE ON IMAGE PROCESSING, ICIP, 2023, : 36 - 40
  • [32] No-Reference Stereoscopic Image Quality Assessment Using Natural Scene Statistics
    Li, Yanqing
    Hu, Xinping
    2017 2ND INTERNATIONAL CONFERENCE ON MULTIMEDIA AND IMAGE PROCESSING (ICMIP), 2017, : 123 - 127
  • [33] DEEP LEARNING AND CYCLOPEAN VIEW FOR NO-REFERENCE STEREOSCOPIC IMAGE QUALITY ASSESSMENT
    Messai, Oussama
    Hachouf, Fella
    Seghir, Zianou Ahmed
    2018 INTERNATIONAL CONFERENCE ON SIGNAL, IMAGE, VISION AND THEIR APPLICATIONS (SIVA), 2018,
  • [34] A Novel Quality Assessment of Transmitted 3D Videos Based on Binocular Rivalry Impact
    Hasan, Md. Mehedi
    Arnold, John F.
    Frater, Michael R.
    2015 PICTURE CODING SYMPOSIUM (PCS) WITH 2015 PACKET VIDEO WORKSHOP (PV), 2015, : 297 - 301
  • [35] A NEW NO-REFERENCE STEREOSCOPIC IMAGE QUALITY ASSESSMENT BASED ON OCULAR DOMINANCE THEORY AND DEGREE OF PARALLAX
    Gu, Ke
    Zhai, Guangtao
    Yang, Xiaokang
    Zhang, Wenjun
    2012 21ST INTERNATIONAL CONFERENCE ON PATTERN RECOGNITION (ICPR 2012), 2012, : 206 - 209
  • [36] A No-Reference Quality Assessment Model for Screen Content Videos via Hierarchical Spatiotemporal Perception
    Liu, Zhihong
    Zeng, Huanqiang
    Chen, Jing
    Ding, Rui
    Shi, Yifan
    Hou, Junhui
    IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY, 2025, 35 (02) : 1422 - 1435
  • [37] Multi-modal No-Reference Objective Quality Assessment Method for Point Cloud Videos
    Chen, Xiaolei
    Zhang, Yuru
    Wen, Runyu
    PROCEEDINGS OF 2023 7TH INTERNATIONAL CONFERENCE ON ELECTRONIC INFORMATION TECHNOLOGY AND COMPUTER ENGINEERING, EITCE 2023, 2023, : 558 - 564
  • [38] No-Reference Stereoscopic Image Quality Assessment Using Convolutional Neural Network for Adaptive Feature Extraction
    Ding, Yong
    Deng, Ruizhe
    Xie, Xin
    Xu, Xiaogang
    Zhao, Yang
    Chen, Xiaodong
    Krylov, Andrey S.
    IEEE ACCESS, 2018, 6 : 37595 - 37603
  • [39] Learning Sparse Representation for No-Reference Quality Assessment of Multiply Distorted Stereoscopic Images
    Shao, Feng
    Tian, Weijun
    Lin, Weisi
    Jiang, Gangyi
    Dai, Qionghai
    IEEE TRANSACTIONS ON MULTIMEDIA, 2017, 19 (08) : 1821 - 1836
  • [40] No-reference quality assessment of HEVC video streams based on visual memory modelling *
    Banitalebi-Dehkordi, Mehdi
    Ebrahimi-Moghadam, Abbas
    Khademi, Morteza
    Hadizadeh, Hadi
    JOURNAL OF VISUAL COMMUNICATION AND IMAGE REPRESENTATION, 2021, 75 (75)