MSU Video Quality Metrics Benchmark Invitation

[msm1rnov]

Hello! We kindly invite you to participate in our video quality metrics benchmark. You can submit DIIVINE to the benchmark, following the submission steps, described here. The dataset distortions refer to compression artifacts on professional and user-generated content. The full dataset is used to measure methods overall performance, so we do not share it to avoid overfitting. Nevertheless, we provided the open part of it (around 1,000 videos) within our paper "Video compression dataset and benchmark of learning-based video-quality metrics", accepted to NeurIPS 2022.