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Video Quality Assessment Using Spatio-Velocity Contrast Sensitivity Function

机译:使用空速对比敏感度函数的视频质量评估

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Due to the development and popularization of high-definition televisions, digital video cameras, Blu-ray discs, digital broadcasting, IP television and so on, it plays an important role to identify and quantify video quality degradations. In this paper, we propose SV-CIELAB which is an objective video quality assessment (VQA) method using a spatio-velocity contrast sensitivity function (SV-CSF). In SV-CIELAB, motion information in videos is effectively utilized for filtering unnecessary information in the spatial frequency domain. As the filter to apply videos, we used the SV-CSF. It is a modulation transfer function of the human visual system, and consists of the relationship among contrast sensitivities, spatial frequencies and velocities of perceived stimuli. In the filtering process, the SV-CSF cannot be directly applied in the spatial frequency domain because spatial coordinate information is required when using velocity information. For filtering by the SV-CSF, we obtain video frames separated in spatial frequency domain. By using velocity information, the separated frames with limited spatial frequencies are weighted by contrast sensitivities in the SV-CSF model. In SV-CIELAB, the criteria are obtained by calculating image differences between filtered original and distorted videos. For the validation of SV-CIELAB, subjective evaluation experiments were conducted. The subjective experimental results were compared with SV-CIELAB and the conventional VQA methods such as CIELAB color difference, Spatial-CIELAB, signal to noise ratio and so on. From the experimental results, it was shown that SV-CIELAB is a more efficient VQA method than the conventional methods.
机译:由于高清电视,数字摄像机,蓝光光盘,数字广播,IP电视等的发展和普及,它在识别和量化视频质量下降中起着重要的作用。在本文中,我们提出SV-CIELAB,这是一种使用时空对比度对比敏感度函数(SV-CSF)的客观视频质量评估(VQA)方法。在SV-CIELAB中,视频中的运动信息被有效地用于过滤空间频域中不必要的信息。作为应用视频的过滤器,我们使用了SV-CSF。它是人类视觉系统的调制传递函数,由对比度敏感度,空间频率和感知刺激的速度之间的关系组成。在滤波过程中,由于在使用速度信息时需要空间坐标信息,因此无法将SV-CSF直接应用于空间频域。为了通过SV-CSF进行滤波,我们获得了在空间频域中分离的视频帧。通过使用速度信息,通过SV-CSF模型中的对比敏感度对空间频率受限的分离帧进行加权。在SV-CIELAB中,通过计算过滤后的原始视频和失真视频之间的图像差异来获得标准。为了验证SV-CIELAB,进行了主观评估实验。将主观实验结果与SV-CIELAB和常规VQA方法(如CIELAB色差,Spatial-CIELAB,信噪比等)进行比较。从实验结果可以看出,SV-CIELAB是比常规方法更有效的VQA方法。

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