We consider the problem of conducting frame rate dependent video quality assessment (VQA) on videos of diverse frame rates, including high frame rate (HFR) videos. More generally, we study how perceptual quality is affected by frame rate, and how frame rate and compression combine to affect perceived quality. We devise an objective VQA model called Space-Time GeneRalized Entropic Difference (GREED) which analyzes the statistics of spatial and temporal band-pass video coefficients. A generalized Gaussian distribution (GGD) is used to model band-pass responses, while entropy variations between reference and distorted videos under the GGD model are used to capture video quality variations arising from frame rate changes. The entropic differences are calculated across multiple temporal and spatial subbands, and merged using a learned regressor. We show through extensive experiments that GREED achieves state-of-the-art performance on the LIVE-YT-HFR Database when compared with existing VQA models. The features used in GREED are highly generalizable and obtain competitive performance even on standard, non-HFR VQA databases. The implementation of GREED has been made available online: https://github.com/pavancm/GREED.

中文翻译：

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ST-GREED：用于帧速率相关视频质量预测的时空广义熵差


我们考虑对不同帧率的视频（包括高帧率（HFR）视频）进行帧率相关的视频质量评估（VQA）的问题。更一般地说，我们研究帧速率如何影响感知质量，以及帧速率和压缩如何结合起来影响感知质量。我们设计了一个名为时空通用熵差 (GREED) 的客观 VQA 模型，用于分析空间和时间带通视频系数的统计数据。广义高斯分布 (GGD) 用于对带通响应进行建模，而 GGD 模型下参考视频和失真视频之间的熵变化用于捕获帧速率变化引起的视频质量变化。熵差是跨多个时间和空间子带计算的，并使用学习的回归器进行合并。我们通过大量实验证明，与现有 VQA 模型相比，GREED 在 LIVE-YT-HFR 数据库上实现了最先进的性能。 GREED 中使用的功能具有高度通用性，即使在标准的非 HFR VQA 数据库上也能获得具有竞争力的性能。 GREED 的实施已在线提供：https://github.com/pavancm/GREED。