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CVD2014—A Database for Evaluating No-Reference Video Quality Assessment Algorithms

机译:CVD2014-用于评估无参考视频质量评估算法的数据库

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In this paper, we present a new video database: CVD2014—Camera Video Database. In contrast to previous video databases, this database uses real cameras rather than introducing distortions via post-processing, which results in a complex distortion space in regard to the video acquisition process. CVD2014 contains a total of 234 videos that are recorded using 78 different cameras. Moreover, this database contains the observer-specific quality evaluation scores rather than only providing mean opinion scores. We have also collected open-ended quality descriptions that are provided by the observers. These descriptions were used to define the quality dimensions for the videos in CVD2014. The dimensions included sharpness, graininess, color balance, darkness, and jerkiness. At the end of this paper, a performance study of image and video quality algorithms for predicting the subjective video quality is reported. For this performance study, we proposed a new performance measure that accounts for observer variance. The performance study revealed that there is room for improvement regarding the video quality assessment algorithms. The CVD2014 video database has been made publicly available for the research community. All video sequences and corresponding subjective ratings can be obtained from the CVD2014 project page ().
机译:在本文中,我们提出了一个新的视频数据库:CVD2014-摄像机视频数据库。与以前的视频数据库相比,该数据库使用真实的摄像机,而不是通过后期处理引入失真,这导致了视频采集过程中复杂的失真空间。 CVD2014总共包含234个视频,这些视频使用78个不同的摄像机录制。此外,该数据库包含特定于观察者的质量评估得分,而不是仅提供平均意见得分。我们还收集了观察员提供的开放式质量描述。这些描述用于定义CVD2014中视频的质量尺寸。尺寸包括清晰度,颗粒感,色彩平衡,暗淡和混浊。在本文的最后,报告了一种用于预测主观视频质量的图像和视频质量算法的性能研究。对于此性能研究,我们提出了一种新的性能指标,该指标考虑了观察者的差异。性能研究表明,视频质量评估算法仍有改进的空间。 CVD2014视频数据库已向研究社区公开提供。所有视频序列和相应的主观评分均可从CVD2014项目页面()获得。

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