论文标题
ST-GRED：帧速率依赖视频质量预测的时空广义熵差
ST-GREED: Space-Time Generalized Entropic Differences for Frame Rate Dependent Video Quality Prediction
论文作者
论文摘要
我们考虑了有关不同框架速率（包括高帧速率（HFR）视频）的视频的依赖视频质量评估（VQA）的问题。更普遍地，我们研究了感知质量如何受帧速率的影响，以及框架速率和压缩如何结合以影响感知的质量。我们设计了一个名为“时空通用熵差”（Greed）的客观VQA模型，该模型分析了空间和时间频道视频系数的统计数据。广义高斯分布（GGD）用于模拟带通响应，而GGD模型下参考和扭曲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。熵差异是在多个时间和空间子带之间计算的，并使用学习的回归器合并。我们通过广泛的实验来展示与现有VQA模型相比，贪婪在Live-YT-HFR数据库中实现最先进的性能。贪婪中使用的功能是高度概括的，即使在标准的非HFR VQA数据库上也获得了竞争性能。贪婪的实施已在线提供：https：//github.com/pavancm/greed
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
