构建首个神经视频编码主观评分数据集，助力新型压缩质量评估。
Neural video codecs quality assessment dataset and benchmark
- 通过众包打分收集神经与传统编码视频的成对主观体验数据。
- 覆盖多种编码器和复杂场景，支持高质量评估指标开发。
- 适合从事视频压缩、质量评估或深度学习应用的研究者使用。
视频流量占全球网络流量的重要份额。为减少其体积，视频编码技术不断发展。尽管传统视频编码已取得显著进展，神经视频编码（NVCs）作为基于深度学习的新方法近年兴起，带来新的质量评估挑战，尤其针对其引入的新型时序压缩机制。本文提出一个大规模主观评分数据集，包含经神经与传统编码器压缩的视频，主观分数通过众包式成对比较获取。该数据集为面向神经视频编码的质量评估指标开发与基准测试提供了宝贵资源。数据集可于 https://videoprocessing.github.io/nvc-dataset-benchmark 获取。
原文摘要 · Abstract (English)
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
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