7 citations · 12 across the 8 of their papers we have counts for
7 papers · 1 filter
SR-Prominence: A Crowdsourced Protocol and Dataset Suite for Perceptually-Weighted Super-Resolution Artifact Evaluation
Ivan Molodetskikh, Kirill Malyshev, Mark Mirgaleev +3
Modern image super-resolution methods generate detailed, visually appealing results, but they often introduce visual artifacts: unnatural patterns and texture distortions that degr…
Exploring Real-Time Super-Resolution: Benchmarking and Fine-Tuning for Streaming Content
Evgeney Bogatyrev, Khaled Abud, Ivan Molodetskikh +2
Recent advancements in real-time super-resolution have enabled higher-quality video streaming, yet existing methods struggle with the unique challenges of compressed video content.…
Prominence-Aware Artifact Detection and Dataset for Image Super-Resolution
Ivan Molodetskikh, Kirill Malyshev, Mark Mirgaleev +3
Generative single-image super-resolution (SISR) is advancing rapidly, yet even state-of-the-art models produce visual artifacts: unnatural patterns and texture distortions that deg…
Machine vision-aware quality metrics for compressed image and video assessment
Mikhail Dremin, Konstantin Kozhemyakov, Ivan Molodetskikh +3
A main goal in developing video-compression algorithms is to enhance human-perceived visual quality while maintaining file size. But modern video-analysis efforts such as detection…
Combining Contrastive and Supervised Learning for Video Super-Resolution Detection
Viacheslav Meshchaninov, Ivan Molodetskikh, Dmitriy Vatolin
Upscaled video detection is a helpful tool in multimedia forensics, but it is a challenging task that involves various upscaling and compression algorithms. There are many resoluti…
Temporally Coherent Person Matting Trained on Fake-Motion Dataset
Ivan Molodetskikh, Mikhail Erofeev, Andrey Moskalenko +1
We propose a novel neural-network-based method to perform matting of videos depicting people that does not require additional user input such as trimaps. Our architecture achieves…