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Publications in Math-Net.Ru |
Citations |
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2023 |
| 1. |
A. V. Solovev, A. V. Antsiferova, D. S. Vatolin, V. A. Galaktionov, “Development of neural network-based video preprocessing method to increase the VMAF score relative to source video using distillation”, Keldysh Institute preprints, 2023, 066, 11 pp.  |
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2022 |
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D. Yu. Turdakov, A. I. Avetisyan, K. V. Arkhipenko, A. V. Antsiferova, D. S. Vatolin, S. S. Volkov, A. V. Gasnikov, D. A. Devyatkin, M. D. Drobyshevskiy, A. P. Kovalenko, M. I. Krivonosov, N. V. Lukashevich, V. A. Malykh, S. I. Nikolenko, I. V. Oseledets, A. I. Perminov, I. V. Sochenkov, M. M. Tihomirov, A. N. Fedotov, M. Yu. Khachay, “Trusted artificial intelligence: challenges and promising solutions”, Dokl. RAN. Math. Inf. Proc. Upr., 508 (2022), 13–18  ; Dokl. Math., 106:suppl. 1 (2022), S9–S13 |
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| 3. |
M. V. Siniukov, D. L. Kulikov, D. S. Vatolin, V. A. Galaktionov, “Applicability limitations of differentiable full-reference image quality metrics”, Keldysh Institute preprints, 2022, 086, 15 pp. |
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E. N. Bogatyrev, I. A. Molodetskikh, D. S. Vatolin, V. A. Galaktionov, “Compressed video quality assessment for super-resolution: a benchmark and a quality metric”, Keldysh Institute preprints, 2022, 081, 24 pp. |
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V. P. Meshchaninov, I. A. Molodetskikh, D. S. Vatolin, A. G. Voloboy, “Combining contrastive and supervised learning for video super-resolution detection”, Keldysh Institute preprints, 2022, 080, 13 pp. |
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M. S. Zhelezov, E. V. Lyapustin, A. V. Moskalenko, D. S. Vatolin, A. G. Voloboy, “Choosing the best projection for 360$^\circ$ video”, Keldysh Institute preprints, 2022, 079, 9 pp. |
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2020 |
| 7. |
A. V. Zvezdakova, D. L. Kulikov, S. V. Zvezdakov, D. S. Vatolin, “BSQ-rate: a new approach for video-codec performance comparison and drawbacks of current solutions”, Proceedings of ISP RAS, 32:1 (2020), 89–108 |
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