论文标题

BVI-CC:用于视频压缩和质量评估研究的数据集

BVI-CC: A Dataset for Research on Video Compression and Quality Assessment

论文作者

Katsenou, Angeliki V., Zhang, Fan, Afonso, Mariana, Dimitrov, Goce, Bull, David R.

论文摘要

在过去的几年中,视频技术风景非常生动,新颖的视频编码技术有望改善与最先进技术相比的压缩性能。尽管有许多视频数据集可用,但仍需要广泛的参数空间的代表性内容以及对从非四方面的主观评估编码内容的变化。根据此要求,本文具有数据集,即BVI-CC。部署了三个视频编解码器来创建编码序列的变化:高效率视频编码(HEVC)测试模型(HM),AOMEDIA视频1(AV1)和Versatile视频编码(VVC)测试模型(VTM)。精心选择了九个源视频序列,以在时空域中提供多样性和代表性。序列的不同空间分辨率版本是由所有三个编解码器以预定义的目标比率创建和编码的。用常用的客观质量指标评估了编解码器的压缩效率,并且还通过心理物理实验评估了其重建内容的主观质量。此外,使用客观和主观评估评估了自适应比特率(跨空间分辨率跨空间分辨率优化)测试案例。最后,检查了测试器的计算复杂性。所有数据均作为数据集的一部分公开提供,可用于编码性能评估和视频质量指标开发。

The video technology scenery has been very vivid over the past years, with novel video coding technologies introduced that promise improved compression performance over state-of-the-art technologies. Despite the fact that a lot of video datasets are available, representative content of the wide parameter space along with subjective evaluations of variations of encoded content from an unpartial end is required. In response to this requirement, this paper features a dataset, the BVI-CC. Three video codecs were deployed to create the variations of the encoded sequences: High Efficiency Video Coding (HEVC) Test Model (HM), AOMedia Video 1 (AV1), and Versatile Video Coding (VVC) Test Model (VTM). Nine source video sequences were carefully selected to offer both diversity and representativeness in the spatio-temporal domain. Different spatial resolution versions of the sequences were created and encoded by all three codecs at pre-defined target bit rates. The compression efficiency of the codecs was evaluated with commonly used objective quality metrics, and the subjective quality of their reconstructed content was also evaluated through psychophysical experiments. Additionally, an adaptive bit rate (convex hull rate-distortion optimization across spatial resolutions) test case was assessed using both objective and subjective evaluations. Finally, the computational complexities of the tested codecs were examined. All data have been made publicly available as part of the dataset, which can be used for coding performance evaluation and video quality metric development.

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