Perceptual video coding (PVC) optimization has been an important video coding technique, which can be consistent with the perception characteristics of the human visual system (HVS). Currently, PVC schemes incorporating the just noticeable distortion (JND) model can obtain better performance gain in all PVC schemes. To further accelerate the JND computation for real-time video coding applications (e.g. surveillance video coding and conference video coding), this paper proposes a fast perceptual surveillance video coding (PSVC) scheme based on background model-driven JND estimation method. First, to utilize the surveillance scene characteristics, the computation complexity of JND estimation can be significantly decreased by reusing the content complexity of background regions. Then we apply the perceptive video coding scheme into the background modeling-based surveillance video codec. The proposed scheme adopts background modeling frame as background anchor. Experimental results show that the proposed scheme can yield remarkable time saving of 42.33% maximum and on average 34.76% with approximate bitrate reductions and similar subjective quality, compared to HEVC and other state-of-the-art schemes.

中文翻译：

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基于背景模型驱动JND估计的快速感知监控视频编码（PSVC）

感知视频编码（PVC）优化一直是一种重要的视频编码技术，可以与人类视觉系统（HVS）的感知特性保持一致。目前，结合了可察觉失真（JND）模型的 PVC 方案可以在所有 PVC 方案中获得更好的性能增益。为了进一步加速实时视频编码应用（例如监控视频编码和会议视频编码）的JND计算，本文提出了一种基于背景模型驱动的JND估计方法的快速感知监控视频编码（PSVC）方案。首先，利用监控场景特征，通过重用背景区域的内容复杂度，可以显着降低JND估计的计算复杂度。然后我们将感知视频编码方案应用到基于背景建模的监控视频编解码器中。所提出的方案采用背景建模框架作为背景锚。实验结果表明，与 HEVC 和其他最先进的方案相比，所提出的方案可以节省最多 42.33% 和平均 34.76% 的显着时间，同时具有近似的比特率降低和相似的主观质量。