High frame rate videos are increasingly getting popular in recent years, driven by the strong requirements of the entertainment and streaming industries to provide high quality of experiences to consumers. To achieve the best trade-offs between the bandwidth requirements and video quality in terms of frame rate adaptation, it is imperative to understand the effects of frame rate on video quality. In this direction, we devise a novel statistical entropic differencing method based on a Generalized Gaussian Distribution model expressed in the spatial and temporal band-pass domains, which measures the difference in quality between reference and distorted videos. The proposed design is highly generalizable and can be employed when the reference and distorted sequences have different frame rates. Our proposed model correlates very well with subjective scores in the recently proposed LIVE-YT-HFR database and achieves state of the art performance when compared with existing methodologies.

中文翻译：

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通过熵差分捕获视频帧速率变化

近年来，在娱乐和流媒体行业为消费者提供高质量体验的强烈要求的推动下，高帧率视频越来越受欢迎。为了在帧速率自适应方面实现带宽要求和视频质量之间的最佳权衡，必须了解帧速率对视频质量的影响。在这个方向上，我们设计了一种基于在空间和时间带通域中表达的广义高斯分布模型的新型统计熵差分方法，该方法测量参考视频和失真视频之间的质量差异。所提出的设计具有高度的通用性，可以在参考序列和失真序列具有不同的帧速率时使用。