, Russian Federation
, Russian Federation
Lomonosov Moscow State Universit
, Russian Federation
, Russian Federation
GRNTI 50.07 Теоретические основы вычислительной техники
BBK 3297 Вычислительная техника
Video quality measurement takes an important role in many applications. Full-reference quality metrics which are usually used in video codecs comparisons are expected to reflect any changes in videos. In this article, we consider different color corrections of compressed videos which increase the values of full-reference metric VMAF and almost don’t decrease other widely-used metric SSIM. The proposed video contrast enhancement approach shows the metric in-applicability in some cases for video codecs comparisons, as it may be used for cheating in the comparisons via tuning to improve this metric values.
video quality, quality measuring, video-codec comparison, quality tuning, reference metrics, colorcorrection
1. Cisco Visual Networking Index: Forecast andMethodology. 2016-2021.
2. HEVC Video Codec Comparison 2018(Thirteen MSU Video Codec Comparison)http://compression.ru/video/codec_ comparison/hevc_2018/
3. MSU Quality Measurement Tool: Download Pagehttp://compression.ru/video/quality_ measure/vqmt_download.html
4. Perceptual Video Quality Metrics: Are theyReady for the Real World? Available online: https://www.ittiam.com/perceptual-videoquality-metrics-ready-real-world
5. VMAF: Perceptual video quality assessment based onmulti-method fusion, Netflix, Inc., 2017 https://github.com/Netflix/vmaf.
6. Xiph.org Video Test Media derf’s collection.https://media.xiph.org/video/derf/
7. C. G. Bampis, Z. Li, and A. C. Bovik, “Spatiotemporal feature integration and model fusion for fullreference video quality assessment,” in IEEETransactions on Circuits and Systems for VideoTechnology, 2018.
8. C. Chen, S. Inguva, A. Rankin, and A. Kokaram, “Asubjective study for the design of multiresolution ABR video streams with the VP9codec,” in Electronic Imaging, 2016(2), pp. 1-5.
9. S. Chikkerur, V. Sundaram, M. Reisslein, and L. J.Karam, “Objective video quality assessment meth-ods:A classification, review, and performancecomparison,” in IEEE Transactions on Broadcast-ing,57(2), pp. 165–182, 2011.
10. K. Deb, A. Pratap, S. Agarwal, and T. A. M.T. Meyarivan, “A fast and elitist multiobjectivegenetic algorithm: NSGA-II,” in IEEE transactions on evolutionary computation, 6(2),pp.182-197, 2002.
11. S. Li, F. Zhang, L. Ma, and K. N. Ngan, “Imagequality assessment by separately evaluating detaillosses and additive impairments”, in IEEE Transactions on Multimedia, 2011, 13(5), pp. 935-949.
12. H. R. Sheikh and A. C. Bovik, “Image informa-tionand visual quality,” in IEEE International Conferenceon Acoustics, Speech, and Signal Pro-cessing, 2004,. 3. – . iii-709.
13. S. van der Walt, J. L. Schonberger, J. NunezIglesias, F. Boulogne, J. D. Warner, N. Yager, E.Gouillart, T. Yu, and the scikit-image contributors. scikit-image: Image processing in Python. PeerJ 2:e453, 2014.