| Hacking vmaf with video color and contrast distortion A Zvezdakova, S Zvezdakov, D Kulikov, D Vatolin arXiv preprint arXiv:1907.04807, 2019 | 18 | 2019 |
| BSQ-rate: a new approach for video-codec performance comparison and drawbacks of current solutions AV Zvezdakova, DL Kulikov, SV Zvezdakov, DS Vatolin Programming and computer software 46, 183-194, 2020 | 17 | 2020 |
| Machine-Learning-Based Method for Content-Adaptive Video Encoding S Zvezdakov, D Kondranin, D Vatolin 2021 Picture Coding Symposium (PCS), 1-5, 2021 | 8 | 2021 |
| Machine-Learning-Based Method for Finding Optimal Video-Codec Configurations Using Physical Input-Video Features. R Kazantsev, S Zvezdakov, DS Vatolin DCC, 374, 2020 | 4 | 2020 |
| Application of physical video features in classification problem R Kazantsev, S Zvezdakov, D Vatolin International Journal of Open Information Technologies 7 (5), 33-38, 2019 | 4 | 2019 |
| Video distortion method for VMAF quality values increasing A Antsiferova, D Kulikov, D Vatolin, S Zvezdakov CoRR, 2019 | 3 | 2019 |
| Iterative Machine-Learning-Based Method of Selecting Encoder Parameters for Speed-Bitrate Tradeoff S Zvezdakov, A Solovyov, D Vatolin 2022 Data Compression Conference (DCC), 01-01, 2022 | | 2022 |
| Automatic Detection and Estimation of Color Discrepancies in S3D Using Confidence Maps S Grokholsky, S Lavrushkin, S Zvezdakov, D Vatolin International Journal of Open Information Technologies 5 (5), 1-8, 2017 | | 2017 |
| Machine-Learning-Based Method for Finding Optimal Video-Codec Configurations Using Physical Input-Video R Kazantsev, S Zvezdakov, D Vatolin | | |
