A spatio-temporal saliency-based frame rate up-conversion (FRUC) approach is proposed, which achieves better quality of interpolated frames and invalidates existing texture variation-based FRUC detectors. A spatio-temporal saliency model is designed to select salient frames. After obtaining initial motion vector field by texture- and color-based bilateral motion estimation, two motion vector refining (MVR) schemes are adopted for high and low saliency frames to hierarchically refine the motion vectors, respectively. To produce high-quality interpolated frames, image enhancement are performed for salient frames after frame interpolation. Due to distinct MVR schemes, there are different degrees of texture information in interpolated frames. Some edge and texture information is supplemented into salient frames as post-processing, which can invalidate existing texture variation-based FRUC detectors. Experimental results show that the proposed approach outperforms state-of-the-art works in both objective and subjective qualities of interpolated frames, and achieves the purpose of FRUC anti-forensics.

中文翻译：

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基于时空显着性的帧速率上转换运动矢量细化

提出了一种基于时空显着性的帧速率上转换（FRUC）方法，该方法可以实现更好的插值帧质量，并使现有的基于纹理变化的 FRUC 检测器无效。时空显着性模型旨在选择显着帧。在通过基于纹理和颜色的双边运动估计获得初始运动矢量场后，对高显着性帧和低显着性帧采用两种运动矢量细化（MVR）方案分别对运动矢量进行分层细化。为了产生高质量的插值帧，在帧插值之后对显着帧进行图像增强。由于不同的 MVR 方案，插值帧中存在不同程度的纹理信息。一些边缘和纹理信息作为后处理补充到显着帧中，这会使现有的基于纹理变化的 FRUC 检测器失效。实验结果表明，所提出的方法在插值帧的客观和主观质量上都优于最先进的作品，并达到了 FRUC 反取证的目的。