A Python Framework for Objective Visual Quality Assessment
Resumo
This work introduces a Quality Assessment Framework that provides researchers with the flexibility, consistency, and scalability they need to evaluate and compare quality metrics, promoting the reproducibility of results. The framework is open source (Python) and currently has 11 visual quality metrics that use 3 different libraries: Scikit-video, FFmpeg toolkit, and PyMetrikz. It can be easily expanded to include more metrics in the future and allows testing on several quality datasets. To validate it, we tested it on two datasets and compared the results with the results obtained by other authors in the literature. The results are consistent with those reported by external studies. With this evidence, new image/video metrics and datasets can be integrated into this framework. This will allow researchers to compare their methods with a wide number of quality metrics on several datasets in a fast and efficient way.
Referências
H.-C. Soong and P.-Y. Lau, "Video quality assessment: A review of full-referenced, reduced-referenced and no-referenced methods," in 2017 IEEE 13th International Colloquium on Signal Processing & its Applications (CSPA). IEEE, 2017, pp. 232-237.
Q. Fan, W. Luo, Y. Xia, G. Li, and D. He, "Metrics and methods of video quality assessment: a brief review," Multimedia Tools and Applications, vol. 78, no. 22, pp. 31 019-31 033, 2019.
J. Geraghty, J. Li, A. Ragano, and A. Hines, "Aqp: An open modular python platform for objective speech and audio quality metrics," arXiv preprint arXiv:2110.13589, 2021.
A. V. Murthy and L. J. Karam, "A matlab-based framework for image and video quality evaluation," in 2010 Second International Workshop on Quality of Multimedia Experience (QoMEX), 2010, pp. 242-247.
B. García, F. Gortázar, M. Gallego, and A. Hines, "Assessment of qoe for video and audio in webrtc applications using full-reference models," Electronics, vol. 9, no. 3, p. 462, 2020.
Z. Wang, A. C. Bovik, H. R. Sheikh, and E. P. Simoncelli, "Image quality assessment: from error visibility to structural similarity," IEEE transactions on image processing, vol. 13, no. 4, pp. 600-612, 2004.
Z. Wang, E. P. Simoncelli, and A. C. Bovik, "Multiscale structural similarity for image quality assessment," in The Thrity-Seventh Asilomar Conference on Signals, Systems & Computers, 2003, vol. 2. Ieee, 2003, pp. 1398-1402.
A. Mittal, R. Soundararajan, and A. C. Bovik, "Making a "completely blind" image quality analyzer," IEEE Signal processing letters, vol. 20, no. 3, pp. 209-212, 2012.
J. Y. Lin, T.-J. Liu, E. C.-H. Wu, and C.-C. J. Kuo, "A fusion-based video quality assessment (fvqa) index," in Signal and Information Processing Association Annual Summit and Conference (APSIPA), 2014 Asia-Pacific, 2014, pp. 1-5.
Z. Li, A. Aaron, I. Katsavounidis, A. Moorthy, and M. Manohara, "Toward a practical perceptual video quality metric," 2016. [Online]. Available: [link].
H. R. Sheikh and A. C. Bovik, "Image information and visual quality," IEEE Transactions on image processing, vol. 15, no. 2, pp. 430-444, 2006.
——, "A visual information fidelity approach to video quality assessment," in The first international workshop on video processing and quality metrics for consumer electronics, vol. 7, no. 2. sn, 2005, pp. 2117-2128.
F. Zhang, S. Li, L. Ma, Y. C. Wong, and K. N. Ngan, "Ivp subjective quality video database," The Chinese University of Hong Kong, http://ivp.ee.cuhk.edu.hk/research/database/subjective, 2011.
H. J. K. James Algina, "Comparing squared multiple correlation coefficients: Examination of a confidence interval and a test significance." Psychological Methods, no. 1939-1463, pp. 76-83, 1999.
P. Bobko, Correlation and regression: Applications for industrial organizational psychology and management (2nd ed.). CA: Sage Publications, 2001.
C. R. Helmrich, M. Siekmann, S. Becker, S. Bosse, D. Marpe, and T. Wiegand, "Xpsnr: A low-complexity extension of the perceptually weighted peak signal-to-noise ratio for high-resolution video quality assessment," in ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2020, pp. 2727- 2731.
L. Lin, J. Yang, Z. Wang, L. Zhou, W. Chen, and Y. Xu, "Compressed video quality index based on saliency-aware artifact detection," Sensors, vol. 21, no. 19, 2021. [Online]. Available: https://www.mdpi.com/1424-8220/21/19/6429
J. Wu, Y. Liu, W. Dong, G. Shi, and W. Lin, "Quality assessment for video with degradation along salient trajectories," IEEE Transactions on Multimedia, vol. 21, no. 11, pp. 2738-2749, 2019.
Y. Liu, J. Wu, L. Li, W. Dong, J. Zhang, and G. Shi, "Spatiotemporal representation learning for blind video quality assessment," IEEE Transactions on Circuits and Systems for Video Technology, vol. 32, no. 6, pp. 3500-3513, 2021.