13 citations · 47 across the 35 of their papers we have counts for
5 papers · 1 filter
Comparing the Robustness of Modern No-Reference Image- and Video-Quality Metrics to Adversarial Attacks
Anastasia Antsiferova, Khaled Abud, Aleksandr Gushchin +3
Nowadays, neural-network-based image- and video-quality metrics perform better than traditional methods. However, they also became more vulnerable to adversarial attacks that incre…
BASED: Benchmarking, Analysis, and Structural Estimation of Deblurring
Nikita Alutis, Egor Chistov, Mikhail Dremin +1
This paper discusses the challenges of evaluating deblurring-methods quality and proposes a reduced-reference metric based on machine learning. Traditional quality-assessment metri…
Fast Adversarial CNN-based Perturbation Attack on No-Reference Image- and Video-Quality Metrics
Ekaterina Shumitskaya, Anastasia Antsiferova, Dmitriy Vatolin
Modern neural-network-based no-reference image- and video-quality metrics exhibit performance as high as full-reference metrics. These metrics are widely used to improve visual qua…
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…
Color Mismatches in Stereoscopic Video: Real-World Dataset and Deep Correction Method
Egor Chistov, Nikita Alutis, Dmitriy Vatolin
Stereoscopic videos can contain color mismatches between the left and right views due to minor variations in camera settings, lenses, and even object reflections captured from diff…