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Dmitriy S. Vatolin

4 accepted papers

2026

Exploring Real-Time Super-Resolution: Benchmarking and Fine-Tuning for Streaming Content

ICLR 2026poster

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. Commonly used datasets do not accurately reflect the characteristics of streaming media, limiting the relevance of curr…

Cited by 0SourcecodeScholar
2025

Guardians of Image Quality: Benchmarking Defenses Against Adversarial Attacks on Image Quality Metrics

ICML 2025poster

Modern neural-network-based Image Quality Assessment (IQA) metrics are vulnerable to adversarial attacks, which can be exploited to manipulate search engine rankings, benchmark results, and content quality assessments, raising concerns about the reliability of IQA metrics in critical applications. T…

2024

IOI: Invisible One-Iteration Adversarial Attack on No-Reference Image- and Video-Quality Metrics

ICML 2024poster

No-reference image- and video-quality metrics are widely used in video processing benchmarks. The robustness of learning-based metrics under video attacks has not been widely studied. In addition to having success, attacks on metrics that can be employed in video processing benchmarks must be fast a…

2022

Video compression dataset and benchmark of learning-based video-quality metrics

NeurIPS 2022accept

Video-quality measurement is a critical task in video processing. Nowadays, many implementations of new encoding standards - such as AV1, VVC, and LCEVC - use deep-learning-based decoding algorithms with perceptual metrics that serve as optimization objectives. But investigations of the performance…