发表机构
AI Center, Lomonosov Moscow State University; MSU Institute for Artificial Intelligence; Lomonosov Moscow State University（罗蒙诺索夫莫斯科国立大学人工智能中心; 莫斯科国立大学人工智能研究所; 罗蒙诺索夫莫斯科国立大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对神经视频编解码器（NVCs）时间压缩范式的质量评估需求，该研究构建了含NVCs与传统编解码器压缩视频的大规模主观数据集，通过众包成对比较获取评分，为相关视频质量指标的开发与基准测试提供了资源。
AI 中文摘要
视频流量占全球网络流量的很大一部分，为减少其流量，视频编解码器已被开发并持续改进。尽管行业在传统视频编码方面取得了显著进展，但神经视频编解码器（NVCs）作为一种将深度学习应用于视频压缩的新方法最近出现，这给压缩质量评估带来了新挑战，而压缩质量评估对这类编解码器的进一步发展和改进至关重要，尤其需要评估NVCs引入的新型时间压缩范式。本研究提出了一个由神经和传统视频编解码器压缩的视频组成的大规模主观数据集，主观评分通过众包成对比较收集，该数据集为开发和评估适用于神经视频编解码器的视频质量指标提供了宝贵资源，数据集可通过以下链接获取：this https URL
英文摘要
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
