论文标题
使用关键点不可知的频率选择网格到网格重新采样的帧速率上转换
Frame Rate Up-Conversion Using Key Point Agnostic Frequency-Selective Mesh-to-Grid Resampling
论文作者
论文摘要
在许多应用领域中，需要高帧速率。在许多情况下，必须提高已经捕获的视频的框架重复率，帧速率上转换（FRUC）引起了很高的关注。我们进行了运动补偿方法。从两个相邻的帧，估计运动，相邻的像素沿着运动向量向框架移动到框架中以进行重建。为了显示，这些不规则分布的网格像素必须被重新采样到规则间隔的网格位置。我们将基于模型的关键点不可知的频率选择性网格到网格重采样（AFSMR）用于此任务，并证明AFSMR最适合包含具有不同密度的不规则网格的应用。与已经具有较高性能的频率选择网格重新采样（FSMR）相比，AFSMR最多可增值3.2 dB。此外，相对于FSMR，AFSMR将运行时间增加11倍。
High frame rates are desired in many fields of application. As in many cases the frame repetition rate of an already captured video has to be increased, frame rate up-conversion (FRUC) is of high interest. We conduct a motion compensated approach. From two neighboring frames, the motion is estimated and the neighboring pixels are shifted along the motion vector into the frame to be reconstructed. For displaying, these irregularly distributed mesh pixels have to be resampled onto regularly spaced grid positions. We use the model-based key point agnostic frequency-selective mesh-to-grid resampling (AFSMR) for this task and show that AFSMR works best for applications that contain irregular meshes with varying densities. AFSMR gains up to 3.2 dB in contrast to the already high performing frequency-selective mesh-to-grid resampling (FSMR). Additionally, AFSMR increases the run time by a factor of 11 relative to FSMR.
