7 citations · 14 across the 10 of their papers we have counts for
11 papers
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…
JPEG AI Image Compression Visual Artifacts: Detection Methods and Dataset
Daria Tsereh, Mark Mirgaleev, Ivan Molodetskikh +2
Learning-based image compression methods have improved in recent years and started to outperform traditional codecs. However, neural-network approaches can unexpectedly introduce v…
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…
AIM 2024 Challenge on Video Super-Resolution Quality Assessment: Methods and Results
Ivan Molodetskikh, Artem Borisov, Dmitriy Vatolin +24
This paper presents the Video Super-Resolution (SR) Quality Assessment (QA) Challenge that was part of the Advances in Image Manipulation (AIM) workshop, held in conjunction with E…
Can No-Reference Quality-Assessment Methods Serve as Perceptual Losses for Super-Resolution?
Egor Kashkarov, Egor Chistov, Ivan Molodetskikh +1
Perceptual losses play an important role in constructing deep-neural-network-based methods by increasing the naturalness and realism of processed images and videos. Use of perceptu…
SR+Codec: a Benchmark of Super-Resolution for Video Compression Bitrate Reduction
Evgeney Bogatyrev, Ivan Molodetskikh, Dmitriy Vatolin
In recent years, there has been significant interest in Super-Resolution (SR), which focuses on generating a high-resolution image from a low-resolution input. Deep learning-based…
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…
Perceptually Motivated Method for Image Inpainting Comparison
Ivan Molodetskikh, Mikhail Erofeev, Dmitry Vatolin
The field of automatic image inpainting has progressed rapidly in recent years, but no one has yet proposed a standard method of evaluating algorithms. This absence is due to the p…