论文标题
通过分辨率 - 自适应流程编码改善深度视频压缩
Improving Deep Video Compression by Resolution-adaptive Flow Coding
论文作者
论文摘要
在基于学习的视频压缩方法中，通过开发新的运动向量（MV）编码器来压缩像素级光流图是一个必要的问题。在这项工作中，我们提出了一个称为“分辨率自适应流动编码（RAFC）”的新框架，以有效地在全球和本地压缩流量图，在该框架上，我们使用多分辨率表示，而不是用于输入流量图和MV Encoder的输出运动特征的单分辨率表示。为了在全球处理复杂或简单的运动模式，我们的框架级方案RAFC框架自动决定为每个视频帧的最佳流程图分辨率。为了应对本地的不同类型的运动模式，我们称为RAFC-Block的块级方案还可以为每个局部运动特征块选择最佳分辨率。此外，将速率延伸标准应用于RAFC框架和RAFC-Block，并选择有效流动编码的最佳运动编码模式。在四个基准数据集HEVC，VTL，UVG和MCL-JCV上进行的全面实验清楚地证明了我们的RAFC框架和RAFC-Block以进行视频压缩后，我们的整体RAFC框架的有效性。
In the learning based video compression approaches, it is an essential issue to compress pixel-level optical flow maps by developing new motion vector (MV) encoders. In this work, we propose a new framework called Resolution-adaptive Flow Coding (RaFC) to effectively compress the flow maps globally and locally, in which we use multi-resolution representations instead of single-resolution representations for both the input flow maps and the output motion features of the MV encoder. To handle complex or simple motion patterns globally, our frame-level scheme RaFC-frame automatically decides the optimal flow map resolution for each video frame. To cope different types of motion patterns locally, our block-level scheme called RaFC-block can also select the optimal resolution for each local block of motion features. In addition, the rate-distortion criterion is applied to both RaFC-frame and RaFC-block and select the optimal motion coding mode for effective flow coding. Comprehensive experiments on four benchmark datasets HEVC, VTL, UVG and MCL-JCV clearly demonstrate the effectiveness of our overall RaFC framework after combing RaFC-frame and RaFC-block for video compression.
