No-reference quality assessment of dynamic sports videos based on a spatiotemporal motion model |
Kim, Hyoung-Gook
(Department of Electronics Convergence Engineering, Kwangwoon University)
Shin, Seung-Su (Department of Electronics Convergence Engineering, Kwangwoon University) Kim, Sang-Wook (Department of Software, Chung-Ang University) Lee, Gi Yong (Department of Electronics Convergence Engineering, Kwangwoon University) |
1 | C. Wang, L. Su, and Q. Huang, CNN-MR for no reference video quality assessment, in Proc. Int. Conf. Inf. Sci. Control Eng. (Changsha, China), July 2017, pp. 224-228. |
2 | M. Zhang et al., Deep residual-network-based quality assessment for SD-OCT retinal images: Preliminary study, in Proc. Medical Imaging 2019: Image Percept., Obs. Perform., Technol. Assess. (San Diego, CA, USA), Mar. 2019, 095214: 1-6. |
3 | V. Hosu et al., The konstanz natural video database (KoNViD-1k), in Proc. Int. Conf. Qual. Multimedia Exper. (QoMEX) (Erfurt, Germany), June 2017, pp. 1-6. |
4 | M. A. Saad and A. C. Bovik, Blind quality assessment of videos using a model of natural scene statistics and motion coherency, in Proc. Conf. Rec. Asilomar Conf. Signals Syst. Comput. (Pacific Grove, CA, USA), Nov. 2012, pp. 332-336. |
5 | M. T. Vega et al., Deep learning for quality assessment in live video streaming, IEEE Signal Process. Lett. 24 (2017), 736-740. DOI |
6 | J. Xu et al., No-reference video quality assessment via feature learning, in Proc. IEEE Int. Conf. Image Process. (Paris, France), Oct. 2014, pp. 491-495. |
7 | S. W. Fu et al., Quality-net: An end-to-end non-intrusive speech quality assessment model based on BLSTM, in Proc. Interspeech (Hyderabad, India), Setp. 2018, pp. 1873-1877. |
8 | H. Mao, R. Netravali, and M. Alizadeh, Neural adaptive video streaming with pensieve, in Proc. Conf. ACM Special Interest Group Data Commun. (New York, NY, USA), Aug. 2017, pp. 197-210. |
9 | P. Yan and X. Mou, Video quality assessment based on correlation between spatiotemporal motion energies, in Proc. Appl. Digit. Image Process. XXXIX (San Diego, CA, USA), Sept. 2016, 997130: 1-12. |
10 | K. Seshadrinathan and A. C. Bovik, Motion tuned spatio-temporal quality assessment of natural videos, IEEE Trans. Image Process. 19 (2010), 335-350. DOI |
11 | C. G. Bampis et al., Recurrent and dynamic models for predicting streaming video quality of experience, IEEE Trans. Image Process. 27 (2018), 3316-3331. DOI |
12 | D. Nilsson and C. Sminchisescu, Semantic video segmentation by gated recurrent flow propagation, in Proc. IEEE Conf. Comput. Vis. Pattern Recognit. (Salt Lake City, UT, USA), June 2018, pp. 6819-6828. |
13 | P. V. Vu and D. M. Chandler, ViS3: An algorithm for video quality assessment via analysis of spatial and spatiotemporal slices, J. Electron. Imaging 23 (2014), no. 1, article no. 01316. |
14 | J. Sogaard, S. Forchhammer, and J. Korhonen, No-reference video quality assessment using codec analysis, IEEE Trans. Circuits Syst. Video Technol. 25 (2015), 1637-1650. DOI |
15 | T. R. Goodall, A. C. Bovik, and N. G. Paulter, Tasking on natural statistics of infrared images, IEEE Trans. Image Process. 25 (2016), 65-79. DOI |
16 | Z. Akhtar and T. H. Falk, Audio-visual multimedia quality assessment: A comprehensive survey, IEEE Access 5 (2017), 21090-21117. DOI |
17 | K. Manasa and S. S. Channappayya, An optical flow-based full reference video quality assessment algorithm, IEEE Trans. Image Process. 25 (2016), 2480-2492. DOI |
18 | C. Keimel, T. Oelbaum, and K. Diepold, No-reference video quality evaluation for high-definition video, in Proc. IEEE Int. Conf. Acoust., Speech Signal Process. (Taipei, Taiwan), Apr. 2009, pp. 1145-1148. |
19 | R. Soundararajan and A. C. Bovik, Video quality assessment by reduced reference spatio-temporal entropic differencing, IEEE Trans. Circuits Syst. Video Technol. 23 (2012), 684-694. DOI |
20 | Q. Bao, R. Huang, and X. Wei, Video quality assessment based on the improved LSTM model, in Image and Graphics, vol. 10667, Springer, Cham, Switzerland, 2017, pp. 313-324. DOI |
21 | Y. Li et al., No-reference video quality assessment with 3D shearlet transform and convolutional neural networks, IEEE Trans. Circuits Syst. Video Technol. 26 (2016), 1044-1057. DOI |
22 | K. Zhu et al., No-reference video quality assessment based on artifact measurement and statistical analysis, IEEE Trans. Circuits Syst. Video Technol. 25 (2014), 533-546. DOI |