MSU Video Quality Metrics Benchmark Invitation

Hello! We kindly invite you to participate in our [video quality metrics benchmark](https://videoprocessing.ai/benchmarks/video-quality-metrics_both.html). You can submit PatchVQ (or any other your metrics) to the benchmark, following the submission steps, described [here](https://videoprocessing.ai/benchmarks/video-quality-metrics.html#submit). 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"](https://arxiv.org/abs/2211.12109), accepted to NeurIPS 2022.

Start with the benchmark submission steps at the linked video-quality-metrics page, then review the benchmark and dataset paper. Done means submitting PatchVQ or another metric to the external benchmark; this issue does not identify a repository file or test to change.

Written by the indexing model from the issue text.