Neural video codecs quality assessment dataset and benchmark
Computer Vision and Pattern Recognition
Summary
The authors explain that video traffic makes up a large part of internet use and that new methods using deep learning, called neural video codecs (NVCs), are being developed to compress videos better. To improve these new codecs, it's important to measure how well they work, especially over time. They created a large collection of videos compressed with both traditional and neural methods and gathered quality ratings from many people online. This dataset helps researchers develop better ways to evaluate neural video codecs.
video compressionvideo codecsneural video codecsdeep learningvideo quality assessmentsubjective scoringpairwise comparisonvideo trafficdatasetbenchmarking
Authors
Nikolay Safonov, Nikita Gornostaev, Alexandra Dubonos, Dmitriy Vatolin
Abstract
Video traffic constitutes a significant share of global web traffic. To reduce its volume, video codecs have been developed and continuously improved. While the industry has achieved substantial progress in traditional video coding, neural video codecs (NVCs) have recently emerged as a new approach that applies deep learning to video compression. This creates new challenges for compression quality assessment, which is essential for the further development and improvement of such codecs. In particular, it is important to evaluate the novel temporal compression paradigms introduced by NVCs. In this work, we present a large-scale subjective dataset of videos compressed with both neural and traditional video codecs. The subjective scores were collected through crowd-sourced pairwise comparisons. The proposed dataset provides a valuable resource for the development and benchmarking of video quality metrics tailored to neural video codecs. The dataset is available at the following link: https://videoprocessing.github.io/nvc-dataset-benchmark