论文标题
关于参数驱动的方法的好处，用于建模和预测压缩视频的满足用户比率
On the benefit of parameter-driven approaches for the modeling and the prediction of Satisfied User Ratio for compressed video
论文作者
论文摘要
直到一定的失真阈值，人眼才能感知图像或视频的小像素变化。在视频压缩的背景下，公正的差异（JND）是人眼可以从中可以感知参考视频和变形/压缩的差异的最小失真级别。满足 - 用户比率（SUR）曲线是观众组各个JND的互补累积分布函数。但是，以前的大多数作品都通过使用源视频和压缩视频中的功能来预测SUR曲线中的每个点，并假设基于组的JND注释遵循高斯分布，这既不实用也不准确。在这项工作中，我们首先比较了SUR曲线建模的各种共同功能。之后，我们提出了一种新颖的参数驱动方法，以从视频功能中预测视频SUR。此外，我们比较了仅源功能（基于SRC）模型和源以及压缩视频功能（基于SRC+PVS）模型的预测结果。
The human eye cannot perceive small pixel changes in images or videos until a certain threshold of distortion. In the context of video compression, Just Noticeable Difference (JND) is the smallest distortion level from which the human eye can perceive the difference between reference video and the distorted/compressed one. Satisfied-User-Ratio (SUR) curve is the complementary cumulative distribution function of the individual JNDs of a viewer group. However, most of the previous works predict each point in SUR curve by using features both from source video and from compressed videos with assumption that the group-based JND annotations follow Gaussian distribution, which is neither practical nor accurate. In this work, we firstly compared various common functions for SUR curve modeling. Afterwards, we proposed a novel parameter-driven method to predict the video-wise SUR from video features. Besides, we compared the prediction results of source-only features based (SRC-based) models and source plus compressed videos features (SRC+PVS-based) models.
