7 citations · 14 across the 10 of their papers we have counts for
4 papers · 1 filter
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…