ABSTRACT
With the fast proliferation of online video sites and social media platforms, user, professionally and occupationally generated content (UGC, PGC, OGC) videos are streamed and explosively shared over the Internet. Consequently, it is urgent to monitor the content quality of these Internet videos to guarantee the user experience. However, most existing modern video quality assessment (VQA) databases only include UGC videos and cannot meet the demands for other kinds of Internet videos with real-world distortions. To this end, we collect 1,072 videos from Youku, a leading Chinese video hosting service platform, to establish the Internet video quality assessment database (Youku-V1K). A special sampling method based on several quality indicators is adopted to maximize the content and distortion diversities within a limited database, and a probabilistic graphical model is applied to recover reliable labels from noisy crowdsourcing annotations. Based on the properties of Internet videos originated from Youku, we propose a spatio-temporal distortion-aware model (STDAM). First, the model works blindly which means the pristine video is unnecessary. Second, the model is familiar with diverse contents by pre-training on the large-scale image quality assessment databases. Third, to measure spatial and temporal distortions, we introduce the graph convolution and attention module to extract and enhance the features of the input video. Besides, we leverage the motion information and integrate the frame-level features into video-level features via a bi-directional long short-term memory network. Experimental results on the self-built database and the public VQA databases demonstrate that our model outperforms the state-of-the-art methods and exhibits promising generalization ability.
- AGH University of Science and Technology. [n. d.]. Video Quality Indicators. http://vq.kt.agh.edu.pl/metrics.html.Google Scholar
- Christos G Bampis, Zhi Li, and Alan C Bovik. 2018. Spatiotemporal feature integration and model fusion for full reference video quality assessment. IEEE Transactions on Circuits and Systems for Video Technology, Vol. 29, 8 (2018), 2256--2270.Google ScholarCross Ref
- BT, RECOMMENDATION ITU-R. 2002. Methodology for the subjective assessment of the quality of television pictures. International Telecommunication Union (2002).Google Scholar
- Zhibo Chen, Wei Zhou, and Weiping Li. 2017. Blind stereoscopic video quality assessment: From depth perception to overall experience. IEEE Transactions on Image Processing, Vol. 27, 2 (2017), 721--734.Google ScholarCross Ref
- Sathya Veera Reddy Dendi and Sumohana S Channappayya. 2020. No-Reference Video Quality Assessment Using Natural Spatiotemporal Scene Statistics. IEEE Transactions on Image Processing, Vol. 29 (2020), 5612--5624.Google ScholarCross Ref
- Jia Deng, Wei Dong, Richard Socher, Li-Jia Li, Kai Li, and Li Fei-Fei. 2009. Imagenet: A large-scale hierarchical image database. In 2009 IEEE conference on computer vision and pattern recognition. Ieee, 248--255.Google ScholarCross Ref
- D.G.Eugene. [n. d.]. Understanding the language of the camera. https://www.thehindu.com/in-school/signpost/understanding-the-language-of-the-camera/article8580792.ece.Google Scholar
- Yuming Fang, Hanwei Zhu, Yan Zeng, Kede Ma, and Zhou Wang. 2020. Perceptual Quality Assessment of Smartphone Photography. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 3677--3686.Google ScholarCross Ref
- Gunnar Farneback. 2003. Two-frame motion estimation based on polynomial expansion. In Scandinavian conference on Image analysis. Springer, 363--370. Google ScholarDigital Library
- Ganlu(Z5130000). [n. d.]. UGC, PGC and OGC? https://z5130000.wordpress.com/2018/06/01/ugc-pgc-and-ogc/.Google Scholar
- Deepti Ghadiyaram and Alan C Bovik. 2017. Perceptual quality prediction on authentically distorted images using a bag of features approach. Journal of vision, Vol. 17, 1 (2017), 32--32.Google ScholarCross Ref
- Deepti Ghadiyaram, Janice Pan, Alan C Bovik, Anush Krishna Moorthy, Prasanjit Panda, and Kai-Chieh Yang. 2017. In-capture mobile video distortions: A study of subjective behavior and objective algorithms. IEEE Transactions on Circuits and Systems for Video Technology, Vol. 28, 9 (2017), 2061--2077.Google ScholarCross Ref
- Franz Götz-Hahn, Vlad Hosu, Hanhe Lin, and Dietmar Saupe. 2019. No-reference video quality assessment using multi-level spatially pooled features. arXiv preprint arXiv:1912.07966 (2019).Google Scholar
- Video Quality Experts Group et al. 2000. Final report from the video quality experts group on the validation of objective models of video quality assessment. In VQEG meeting, Ottawa, Canada, March, 2000 .Google Scholar
- Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. 2016. Deep residual learning for image recognition. In Proceedings of the IEEE conference on computer vision and pattern recognition. 770--778.Google ScholarCross Ref
- Raimund Schatz, and Sebastian Egger. 2011. SOS: The MOS is not enough!. In 2011 third international workshop on quality of multimedia experience. IEEE, 131--136.Google Scholar
- Vlad Hosu, Franz Hahn, Mohsen Jenadeleh, Hanhe Lin, Hui Men, Tamás Szirányi, Shujun Li, and Dietmar Saupe. 2017. The Konstanz natural video database (KoNViD-1k). In 2017 Ninth international conference on quality of multimedia experience (QoMEX). IEEE, 1--6.Google ScholarCross Ref
- P ITU-T RECOMMENDATION. 1999. Subjective video quality assessment methods for multimedia applications. International telecommunication union (1999).Google Scholar
- Diederik P Kingma and Jimmy Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 (2014).Google Scholar
- Jari Korhonen. 2019. Two-level approach for no-reference consumer video quality assessment. IEEE Transactions on Image Processing, Vol. 28, 12 (2019), 5923--5938.Google ScholarCross Ref
- Debarati Kundu, Deepti Ghadiyaram, Alan C Bovik, and Brian L Evans. 2017. No-reference quality assessment of tone-mapped HDR pictures. IEEE Transactions on Image Processing, Vol. 26, 6 (2017), 2957--2971. Google ScholarDigital Library
- Dingquan Li, Tingting Jiang, and Ming Jiang. 2019. Quality assessment of in-the-wild videos. In Proceedings of the 27th ACM International Conference on Multimedia. 2351--2359. Google ScholarDigital Library
- Jing Li, Suiyi Ling, Junle Wang, Zhi Li, and Patrick Le Callet. 2020 b. A probabilistic graphical model for analyzing the subjective visual quality assessment data from crowdsourcing. In Proceedings of the 28th ACM International Conference on Multimedia . Google ScholarDigital Library
- Maosen Li, Siheng Chen, Yangheng Zhao, Ya Zhang, Yanfeng Wang, and Qi Tian. 2020 a. Dynamic Multiscale Graph Neural Networks for 3D Skeleton Based Human Motion Prediction. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 214--223.Google ScholarCross Ref
- Hanhe Lin, Vlad Hosu, and Dietmar Saupe. 2018. KonIQ-10K: Towards an ecologically valid and large-scale IQA database. arXiv preprint arXiv:1803.08489 (2018).Google Scholar
- Kwan-Yee Lin and Guanxiang Wang. 2018. Hallucinated-IQA: No-reference image quality assessment via adversarial learning. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 732--741.Google ScholarCross Ref
- Dong Liu, Rohit Puri, Nagendra Kamath, and Subhabrata Bhattacharya. 2020. Composition-Aware Image Aesthetics Assessment. In The IEEE Winter Conference on Applications of Computer Vision. 3569--3578.Google Scholar
- Wentao Liu, Zhengfang Duanmu, and Zhou Wang. 2018. End-to-End Blind Quality Assessment of Compressed Videos Using Deep Neural Networks.. In ACM Multimedia. 546--554. Google ScholarDigital Library
- Wen Lu, Ran He, Jiachen Yang, Changcheng Jia, and Xinbo Gao. 2019. A spatiotemporal model of video quality assessment via 3D gradient differencing. Information Sciences, Vol. 478 (2019), 141--151.Google ScholarCross Ref
- Anish Mittal, Anush Krishna Moorthy, and Alan Conrad Bovik. 2012a. No-reference image quality assessment in the spatial domain. IEEE Transactions on image processing, Vol. 21, 12 (2012), 4695--4708. Google ScholarDigital Library
- Anish Mittal, Michele A Saad, and Alan C Bovik. 2015. A completely blind video integrity oracle. IEEE Transactions on Image Processing, Vol. 25, 1 (2015), 289--300.Google ScholarDigital Library
- Anish Mittal, Rajiv Soundararajan, and Alan C Bovik. 2012b. Making a "completely blind" image quality analyzer. IEEE Signal processing letters, Vol. 20, 3 (2012), 209--212.Google Scholar
- Anush Krishna Moorthy, Lark Kwon Choi, Alan Conrad Bovik, and Gustavo De Veciana. 2012. Video quality assessment on mobile devices: Subjective, behavioral and objective studies. IEEE Journal of Selected Topics in Signal Processing, Vol. 6, 6 (2012), 652--671.Google ScholarCross Ref
- Mikko Nuutinen, Toni Virtanen, Mikko Vaahteranoksa, Tero Vuori, Pirkko Oittinen, and Jukka H"akkinen. 2016. CVD2014?? database for evaluating no-reference video quality assessment algorithms. IEEE Transactions on Image Processing, Vol. 25, 7 (2016), 3073--3086.Google Scholar
- Stéphane Péchard, Romuald Pépion, and Patrick Le Callet. 2008. Suitable methodology in subjective video quality assessment: a resolution dependent paradigm.Google Scholar
- Michele A Saad, Alan C Bovik, and Christophe Charrier. 2014. Blind prediction of natural video quality. IEEE Transactions on Image Processing, Vol. 23, 3 (2014), 1352--1365. Google ScholarDigital Library
- Mike Schuster and Kuldip K Paliwal. 1997. Bidirectional recurrent neural networks. IEEE transactions on Signal Processing, Vol. 45, 11 (1997), 2673--2681. Google ScholarDigital Library
- Kalpana Seshadrinathan and Alan Conrad Bovik. 2009. Motion tuned spatio-temporal quality assessment of natural videos. IEEE transactions on image processing, Vol. 19, 2 (2009), 335--350. Google ScholarDigital Library
- K. Seshadrinathan and A. C. Bovik. 2011. Temporal hysteresis model of time varying subjective video quality. In 2011 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). 1153--1156.Google Scholar
- Kalpana Seshadrinathan, Rajiv Soundararajan, Alan Conrad Bovik, and Lawrence K Cormack. 2010. Study of subjective and objective quality assessment of video. IEEE transactions on Image Processing, Vol. 19, 6 (2010), 1427--1441. Google ScholarDigital Library
- Karen Simonyan and Andrew Zisserman. 2014. Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556 (2014).Google Scholar
- Zeina Sinno and Alan Conrad Bovik. 2018. Large-scale study of perceptual video quality. IEEE Transactions on Image Processing, Vol. 28, 2 (2018), 612--627.Google ScholarDigital Library
- Shaolin Su, Qingsen Yan, Yu Zhu, Cheng Zhang, Xin Ge, Jinqiu Sun, and Yanning Zhang. 2020. Blindly Assess Image Quality in the Wild Guided by a Self-Adaptive Hyper Network. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 3667--3676.Google ScholarCross Ref
- Zhengzhong Tu, Yilin Wang, Neil Birkbeck, Balu Adsumilli, and Alan C Bovik. 2020. UGC-VQA: Benchmarking Blind Video Quality Assessment for User Generated Content. arXiv preprint arXiv:2005.14354 (2020).Google Scholar
- Phong V Vu and Damon M Chandler. 2014. ViS3: an algorithm for video quality assessment via analysis of spatial and spatiotemporal slices. Journal of Electronic Imaging, Vol. 23, 1 (2014), 013016.Google ScholarCross Ref
- Yilin Wang, Sasi Inguva, and Balu Adsumilli. 2019. Youtube UGC dataset for video compression research. In 2019 IEEE 21st International Workshop on Multimedia Signal Processing (MMSP). IEEE, 1--5.Google ScholarCross Ref
- Zhou Wang, Ligang Lu, and Alan C Bovik. 2004. Video quality assessment based on structural distortion measurement. Signal processing: Image communication, Vol. 19, 2 (2004), 121--132.Google Scholar
- Sanghyun Woo, Jongchan Park, Joon-Young Lee, and In So Kweon. 2018. Cbam: Convolutional block attention module. In Proceedings of the European conference on computer vision (ECCV). 3--19.Google ScholarDigital Library
- Jingtao Xu, Peng Ye, Qiaohong Li, Haiqing Du, Yong Liu, and David Doermann. 2016. Blind image quality assessment based on high order statistics aggregation. IEEE Transactions on Image Processing, Vol. 25, 9 (2016), 4444--4457.Google ScholarDigital Library
- Wufeng Xue, Xuanqin Mou, Lei Zhang, Alan C Bovik, and Xiangchu Feng. 2014. Blind image quality assessment using joint statistics of gradient magnitude and Laplacian features. IEEE Transactions on Image Processing, Vol. 23, 11 (2014), 4850--4862.Google ScholarCross Ref
- Peng Ye, Jayant Kumar, Le Kang, and David Doermann. 2012. Unsupervised feature learning framework for no-reference image quality assessment. In 2012 IEEE conference on computer vision and pattern recognition. IEEE, 1098--1105. Google ScholarDigital Library
- Junyong You, Touradj Ebrahimi, and Andrew Perkis. 2013. Attention driven foveated video quality assessment. IEEE Transactions on Image Processing, Vol. 23, 1 (2013), 200--213. Google ScholarDigital Library
- Lin Zhang, Lei Zhang, and Alan C Bovik. 2015. A feature-enriched completely blind image quality evaluator. IEEE Transactions on Image Processing, Vol. 24, 8 (2015), 2579--2591.Google ScholarDigital Library
- Yu Zhang, Xinbo Gao, Lihuo He, Wen Lu, and Ran He. 2018. Blind video quality assessment with weakly supervised learning and resampling strategy. IEEE Transactions on Circuits and Systems for Video Technology, Vol. 29, 8 (2018), 2244--2255.Google ScholarCross Ref
- Wei Zhou and Zhibo Chen. 2020. Deep Local and Global Spatiotemporal Feature Aggregation for Blind Video Quality Assessment. arXiv preprint arXiv:2009.03411 (2020).Google Scholar
- Wei Zhou, Zhibo Chen, and Weiping Li. 2018. Stereoscopic video quality prediction based on end-to-end dual stream deep neural networks. In Pacific Rim Conference on Multimedia. Springer, 482--492.Google ScholarCross Ref
- Wei Zhou, Qiuping Jiang, Yuwang Wang, Zhibo Chen, and Weiping Li. 2020. Blind quality assessment for image superresolution using deep two-stream convolutional networks. Information Sciences (2020).Google Scholar
- Hancheng Zhu, Leida Li, Jinjian Wu, Weisheng Dong, and Guangming Shi. 2020. MetaIQA: Deep Meta-learning for No-Reference Image Quality Assessment. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 14143--14152.Google ScholarCross Ref
Index Terms
- Perceptual Quality Assessment of Internet Videos
Recommendations
Enriching and localizing semantic tags in internet videos
MM '11: Proceedings of the 19th ACM international conference on MultimediaTagging of multimedia content is becoming more and more widespread as web 2.0 sites, like Flickr and Facebook for images, YouTube and Vimeo for videos, have popularized tagging functionalities among their users. These user-generated tags are used to ...
Motion tuned spatio-temporal quality assessment of natural videos
There has recently been a great deal of interest in the development of algorithms that objectively measure the integrity of video signals. Since video signals are being delivered to human end users in an increasingly wide array of applications and ...
Social and automatic annotation of videos for semantic profiling and content discovery
MM '12: Proceedings of the 20th ACM international conference on MultimediaThis demo presents a system based on social relationships, social knowledge and automatic video and textual content analysis for the discovery of videos in social networks. The system, developed as a web application, allows users to annotate, manually ...
Comments