Evaluating Quality of DIBR-synthesized Views based on Texture and Perceptual Hashing Similarity
Authors: 
Dongsheng Zheng, Huan Zhang, Jiangzhong Cao, Xu Zhang, Ximei Yao, Wing Kuen LingAuthors Info & Claims
Dongsheng Zheng, Huan Zhang, Jiangzhong Cao, Xu Zhang, Ximei Yao, Wing Kuen LingAuthors Info & ClaimsArticle No.: 30, Pages 1 - 6
Abstract
Depth-Image-Based Rendering (DIBR) technology is widely used in 3D video systems to synthesize virtual views. However, the DIBR rendering process tends to introduce local and global distortions, especially local geometric distortion, that will severely affect the perception. In addition, traditional 2D quality metrics may fail to handle this issue since only global distortion is considered. Therefore, in order to evaluate the quality of virtual views more accurately, we propose a full reference DIBR-synthesized views quality assessment model that considers both local and global aspects. Local standard deviation texture images of the reference and distorted images are used to detect local distortions due to local distortions in the virtual view result in a large degree of variation in texture information. The intensity similarity and gradient similarity of the texture images are fused to obtain the final local distortion map. The perceptual hash similarity between the reference and the distorted image is used to quantify the global sharpness due to its powerful frequency domain analysis capability. Depending on the experimental results on the IRCCyN/IVC and IETR databases, the performance of our metric is competitive with the state-of-the-art methods.
References
[1]
Emilie Bosc, Romuald Pepion, Patrick Le Callet, Martin Koppel, Patrick Ndjiki-Nya, Muriel Pressigout, and Luce Morin. 2011. Towards a New Quality Metric for 3-D Synthesized View Assessment. IEEE Journal of Selected Topics in Signal Processing 5, 7 (2011), 1332–1343. https://doi.org/10.1109/JSTSP.2011.2166245
[2]
Christoph Fehn. 2004. Depth-image-based rendering (DIBR), compression, and transmission for a new approach on 3D-TV. In Stereoscopic displays and virtual reality systems XI, Vol. 5291. SPIE, 93–104.
[3]
Ziqing Huang and Shiguang Liu. 2021. Perceptual Hashing With Visual Content Understanding for Reduced-Reference Screen Content Image Quality Assessment. IEEE Transactions on Circuits and Systems for Video Technology 31, 7(2021), 2808–2823. https://doi.org/10.1109/TCSVT.2020.3027001
[4]
Vinit Jakhetiya, Ke Gu, Trisha Singhal, Sharath Chandra Guntuku, Zhifang Xia, and Weisi Lin. 2019. A Highly Efficient Blind Image Quality Assessment Metric of 3-D Synthesized Images Using Outlier Detection. IEEE Transactions on Industrial Informatics 15, 7 (2019), 4120–4128. https://doi.org/10.1109/TII.2018.2888861
[5]
Shuhong Jiao, Huan Qi, and Weisi Lin. 2014. No-Reference Perceptual Image Sharpness Index Using Normalized DCT-based Representation. In 2014 Seventh International Symposium on Computational Intelligence and Design, Vol. 2. 150–153. https://doi.org/10.1109/ISCID.2014.50
[6]
L.K. Lee, Y.B. Yoon, and S.W. Kim. 2018. Reduced-reference quality metric based on local sharpness measure for blurred images. Electronics Letters 54, 17 (2018), 1030–1032. https://doi.org/10.1049/el.2018.5135
[7]
Stefan Leutenegger, Margarita Chli, and Roland Y. Siegwart. 2011. BRISK: Binary robust invariant scalable keypoints. In 2011 International Conference on Computer Vision (ICCV). 2548–2555. https://doi.org/10.1109/ICCV.2011.6126542
[8]
Leida Li, Wenhan Xia, Weisi Lin, Yuming Fang, and Shiqi Wang. 2017. No-Reference and Robust Image Sharpness Evaluation Based on Multiscale Spatial and Spectral Features. IEEE Transactions on Multimedia 19, 5 (2017), 1030–1040. https://doi.org/10.1109/TMM.2016.2640762
[9]
Leida Li, Yu Zhou, Jinjian Wu, Fu Li, and Guangming Shi. 2021. Quality Index for View Synthesis by Measuring Instance Degradation and Global Appearance. IEEE Transactions on Multimedia 23 (2021), 320–332. https://doi.org/10.1109/TMM.2020.2980185
[10]
Suiyi Ling and Patrick Le Callet. 2017. Image Quality Assessment for DIBR Synthesized Views using Elastic Metric. Proceedings of the 25th ACM international conference on Multimedia (2017).
[11]
Suiyi Ling and Patrick Le Callet. 2017. Image quality assessment for free viewpoint video based on mid-level contours feature. 2017 IEEE International Conference on Multimedia and Expo (ICME) (2017), 79–84.
[12]
Saeed Mahmoudpour and Peter Schelkens. 2020. Synthesized View Quality Assessment Using Feature Matching and Superpixel Difference. IEEE Signal Processing Letters 27 (2020), 1650–1654. https://doi.org/10.1109/LSP.2020.3024109
[13]
Ku Pengsen and Yu Zhenming. 2017. Image blurred region detection based on RGB color space information and local standard deviation. In 2017 IEEE 2nd Advanced Information Technology, Electronic and Automation Control Conference (IAEAC). 2177–2181. https://doi.org/10.1109/IAEAC.2017.8054403
[14]
Yifeng Ren, Lei Sun, Guangwei Wu, and Wenzhun Huang. 2017. DIBR-synthesized image quality assessment based on local entropy analysis. In 2017 International Conference on the Frontiers and Advances in Data Science (FADS). IEEE, 86–90.
[15]
Salvatore Serrano, Rafia Mansoor, Muhammad Shahid Farid, Muhammad Hassan Khan, and Asma Maqsood. 2021. A Layered Approach for Quality Assessment of DIBR-Synthesized Images. Wireless Communications and Mobile Computing 2021 (2021), 8377936. https://doi.org/10.1155/2021/8377936
[16]
Norihiro Sugita, Katsuhiro Sasaki, Makoto Yoshizawa, Kei Ichiji, Makoto Abe, Noriyasu Homma, and Tomoyuki Yambe. 2019. Effect of viewing a three-dimensional movie with vertical parallax. Displays 58(2019), 20–26. https://doi.org/10.1016/j.displa.2018.10.007 Special Issue: Visually Induced Motion Sensations.
[17]
Shishun Tian, Lu Zhang, Luce Morin, and Olivier Déforges. 2018. NIQSV+: A No-Reference Synthesized View Quality Assessment Metric. IEEE Transactions on Image Processing 27, 4 (2018), 1652–1664. https://doi.org/10.1109/TIP.2017.2781420
[18]
Shishun Tian, Lu Zhang, Luce Morin, and Olivier Déforges. 2018. SC-IQA: Shift compensation based image quality assessment for DIBR-synthesized views. In 2018 IEEE Visual Communications and Image Processing (VCIP). 1–4. https://doi.org/10.1109/VCIP.2018.8698654
[19]
Shishun Tian, Lu Zhang, Luce Morin, and Olivier Déforges. 2019. A Benchmark of DIBR Synthesized View Quality Assessment Metrics on a New Database for Immersive Media Applications. IEEE Transactions on Multimedia 21, 5 (2019), 1235–1247. https://doi.org/10.1109/TMM.2018.2875307
[20]
Shishun Tian, Lu Zhang, Wenbin Zou, Xia Li, Ting Su, Luce Morin, and Olivier Déforges. 2021. Quality assessment of DIBR-synthesized views: An overview. Neurocomputing 423(2021), 158–178. https://doi.org/10.1016/j.neucom.2020.09.062
[21]
Guangcheng Wang, Nan Guo, Hongyan Liu, Yonghui Zhang, Yazhen Zhou, and Ke Gu. 2021. Evaluating Image Quality of DIBR-synthesized Views in Texture and Depth Domains. In 2021 China Automation Congress (CAC). 5166–5170. https://doi.org/10.1109/CAC53003.2021.9728706
[22]
Guangcheng Wang, Zhongyuan Wang, Ke Gu, Leida Li, Zhifang Xia, and Lifang Wu. 2020. Blind Quality Metric of DIBR-Synthesized Images in the Discrete Wavelet Transform Domain. IEEE Transactions on Image Processing 29 (2020), 1802–1814. https://doi.org/10.1109/TIP.2019.2945675
[23]
Wufeng Xue, Lei Zhang, Xuanqin Mou, and Alan C. Bovik. 2014. Gradient Magnitude Similarity Deviation: A Highly Efficient Perceptual Image Quality Index. IEEE Transactions on Image Processing 23, 2 (2014), 684–695. https://doi.org/10.1109/TIP.2013.2293423
[24]
Mengzhu Yu, Zhenjun Tang, Xianquan Zhang, Bineng Zhong, and Xinpeng Zhang. 2022. Perceptual Hashing with Complementary Color Wavelet Transform and Compressed Sensing for Reduced-Reference Image Quality Assessment. IEEE Transactions on Circuits and Systems for Video Technology (2022). https://doi.org/10.1109/TCSVT.2022.3190273
[25]
Guanghui Yue, Chunping Hou, Ke Gu, Tianwei Zhou, and Guangtao Zhai. 2019. Combining Local and Global Measures for DIBR-Synthesized Image Quality Evaluation. IEEE Transactions on Image Processing 28, 4 (2019), 2075–2088. https://doi.org/10.1109/TIP.2018.2875913
[26]
Huiqing Zhang, Donghao Li, Zhifang Xia, Zichen Wang, and Guangchen Wang. 2020. Energy Loss Estimation Based Reference-Free Quality Assessment of DIBR-Synthesized Views. In 2020 39th Chinese Control Conference (CCC). 3098–3103. https://doi.org/10.23919/CCC50068.2020.9188414
[27]
Zheng Zhang, Yu Liu, Hanlin Tan, Xiaoqing Yi, and Maojun Zhang. 2018. No-Reference Image Sharpness Assessment Using Scale and Directional Models. In 2018 IEEE International Conference on Multimedia and Expo (ICME). 1–6. https://doi.org/10.1109/ICME.2018.8486529
Index Terms
- Evaluating Quality of DIBR-synthesized Views based on Texture and Perceptual Hashing Similarity
Recommendations
An Efficient Quality Metric for DIBR-based 3D Video
HPCC '12: Proceedings of the 2012 IEEE 14th International Conference on High Performance Computing and Communication & 2012 IEEE 9th International Conference on Embedded Software and SystemsIn this paper, an efficient Full-Reference (FR) video quality metric for evaluating the quality of three-dimensional (3D) video generated by Depth Image-Based Rendering (DIBR) technology is proposed. The proposed color quality metric (CQM) is presented ...
Reduced-reference quality assessment of DIBR-synthesized images based on multi-scale edge intensity similarity
Depth-image-based-rendering (DIBR) plays an important role in view synthesis for free-viewpoint videos. The warping process in DIBR causes geometric displacement, which distributes intensively around edges, and the subsequent rendering process results in ...
Perceptual quality assessment for fine-grained compressed images
AbstractRecent years have witnessed the rapid development of image storage and transmission systems, in which image compression plays an important role. Generally speaking, image compression algorithms are developed to ensure good visual ...
Highlights- A full-reference image quality assessment method for fine-grained compressed image.
Comments
Information & Contributors
Information
Published In
December 2022
770 pages
ISBN:9781450398336
DOI:10.1145/3579654
Copyright © 2022 ACM.
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].
Publisher
Association for Computing Machinery
New York, NY, United States
Publication History
Published: 14 March 2023
Check for updates
Author Tags
Qualifiers
- Research-article
- Research
- Refereed limited
Funding Sources
- GuangDong Basic and Applied Basic Research Foundation
- Guangdong Provincial Key Laboratory of Intellectual Property & Big Data
Conference
ACAI 2022
ACAI 2022: 2022 5th International Conference on Algorithms, Computing and Artificial Intelligence
December 23 - 25, 2022
Sanya, China
Acceptance Rates
Overall Acceptance Rate 173 of 395 submissions, 44%
Contributors
Other Metrics
Bibliometrics & Citations
Bibliometrics
Article Metrics
- 0Total Citations
- 53Total Downloads
- Downloads (Last 12 months)17
- Downloads (Last 6 weeks)3
Reflects downloads up to 08 Mar 2025
Other Metrics
Citations
View Options
Login options
Check if you have access through your login credentials or your institution to get full access on this article.
Sign inFull Access
View options
View or Download as a PDF file.
PDFeReader
View online with eReader.
eReaderHTML Format
View this article in HTML Format.
HTML Format