A Perception-Based Hybrid Model for Video Quality Assessment
A Perception-Based Hybrid Model for Video Quality Assessment
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DOI：
10.1109/tcsvt.2015.2428551
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发表时间：
2016-06
影响因子：
8.4
通讯作者：
Fan Zhang;D. Bull
中科院分区：
文献类型：
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作者：
Fan Zhang;D. Bull
众所周知，人类视觉系统（HVS）采用独立的过程（失真检测和伪像感知-也通常被称为近阈值和阈上失真感知）来评估各种失真水平的视频质量。视觉掩蔽效应在视频失真感知中也起着重要作用，特别是在空间和时间纹理中。本文提出了一种新的基于感知的视频质量评价混合模型。这通过使用增强的非线性模型自适应地组合明显的失真和模糊伪影来模拟HVS感知过程。显著失真是通过使用表征纹理掩蔽效应的空间和时间容差图对绝对差异进行阈值化来定义的，并且当失真视频的质量与原始视频的质量相似时，这对质量评估做出了重大贡献。通过计算高频能量变化和运动速度加权估计的模糊伪影的表征被发现进一步提高了度量性能。对于低质量的案例尤其如此。我们的模型的所有阶段利用双树复小波变换的方向选择性和平移不变性。这不仅有助于提高性能，还为新的低复杂度在环应用提供了潜力。我们的方法进行评估的视频质量专家组（VQEG）的充分参考电视第一阶段和实验室的图像和视频工程（LIVE）的视频数据库。由此产生的整体性能优于现有的指标，表现出统计上更好或相当的性能，具有显着降低的复杂性上级。
It is known that the human visual system (HVS) employs independent processes (distortion detection and artifact perception-also often referred to as near-threshold and suprathreshold distortion perception) to assess video quality for various distortion levels. Visual masking effects also play an important role in video distortion perception, especially within spatial and temporal textures. In this paper, a novel perception-based hybrid model for video quality assessment is presented. This simulates the HVS perception process by adaptively combining noticeable distortion and blurring artifacts using an enhanced nonlinear model. Noticeable distortion is defined by thresholding absolute differences using spatial and temporal tolerance maps that characterize texture masking effects, and this makes a significant contribution to quality assessment when the quality of the distorted video is similar to that of the original video. Characterization of blurring artifacts, estimated by computing high frequency energy variations and weighted with motion speed, is found to further improve metric performance. This is especially true for low quality cases. All stages of our model exploit the orientation selectivity and shift invariance properties of the dual-tree complex wavelet transform. This not only helps to improve the performance but also offers the potential for new low complexity in-loop application. Our approach is evaluated on both the Video Quality Experts Group (VQEG) full reference television Phase I and the Laboratory for Image and Video Engineering (LIVE) video databases. The resulting overall performance is superior to the existing metrics, exhibiting statistically better or equivalent performance with significantly lower complexity.