论文标题

在盲视频质量评估中探索视频感知表示的有效性

Exploring the Effectiveness of Video Perceptual Representation in Blind Video Quality Assessment

论文作者

Liao, Liang, Xu, Kangmin, Wu, Haoning, Chen, Chaofeng, Sun, Wenxiu, Yan, Qiong, Lin, Weisi

论文摘要

随着非专家们拍摄的野外视频的快速增长,盲目视频质量评估(VQA)已成为一个具有挑战性且苛刻的问题。尽管为解决这个问题做出了许多努力,但尚不清楚人类视觉系统(HVS)与视频的时间质量如何相关。同时,最近的工作发现,自然视频的框架变成了HV的感知领域,往往会形成表示形式的直接轨迹。通过获得的洞察力,即失真会损害感知的视频质量并导致感知表示的弯曲轨迹,我们提出了一个时间感知质量指数(TPQI),以通过描述表示形式的图形形态来测量时间失真。具体而言,我们首先从HVS的横向遗传核(LGN)和主要视觉区域(V1)中提取视频感知表示,然后测量其轨迹的直率和紧凑性,以量化视频的自然性和内容连续性的降解。实验表明,HVS中的感知表示是一种预测主观时间质量的有效方法,因此TPQI首次可以实现与空间质量度量标准的可比性能,并且在评估具有较大时间变化的视频方面更加有效。我们进一步证明,通过与NIQE(空间质量指标)结合使用,TPQI可以在流行的野外视频数据集中实现最佳性能。更重要的是,除了要评估的视频之外,TPQI不需要任何其他信息,因此可以将其应用于任何数据集,而无需参数调整。源代码可从https://github.com/uolmm/tpqi-vqa获得。

With the rapid growth of in-the-wild videos taken by non-specialists, blind video quality assessment (VQA) has become a challenging and demanding problem. Although lots of efforts have been made to solve this problem, it remains unclear how the human visual system (HVS) relates to the temporal quality of videos. Meanwhile, recent work has found that the frames of natural video transformed into the perceptual domain of the HVS tend to form a straight trajectory of the representations. With the obtained insight that distortion impairs the perceived video quality and results in a curved trajectory of the perceptual representation, we propose a temporal perceptual quality index (TPQI) to measure the temporal distortion by describing the graphic morphology of the representation. Specifically, we first extract the video perceptual representations from the lateral geniculate nucleus (LGN) and primary visual area (V1) of the HVS, and then measure the straightness and compactness of their trajectories to quantify the degradation in naturalness and content continuity of video. Experiments show that the perceptual representation in the HVS is an effective way of predicting subjective temporal quality, and thus TPQI can, for the first time, achieve comparable performance to the spatial quality metric and be even more effective in assessing videos with large temporal variations. We further demonstrate that by combining with NIQE, a spatial quality metric, TPQI can achieve top performance over popular in-the-wild video datasets. More importantly, TPQI does not require any additional information beyond the video being evaluated and thus can be applied to any datasets without parameter tuning. Source code is available at https://github.com/UoLMM/TPQI-VQA.

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