论文标题
用超优先指导模式预测的粗到最深的视频编码
Coarse-to-fine Deep Video Coding with Hyperprior-guided Mode Prediction
论文作者
论文摘要
先前的深视频压缩方法仅使用单一运动补偿策略，并且很少从传统标准（例如H.264/H.265）采用模式预测技术来进行运动和残留压缩。在这项工作中，我们首先提出了一个粗到精细的（C2F）深视频压缩框架，以进行更好的运动补偿，在该框架中，我们以粗到良好的方式进行了运动估计，压缩和补偿。我们的C2F框架可以实现更好的运动补偿结果，而不会显着增加位成本。观察高优势网络中的高优势信息（即平均值和方差值）包含不同斑块的判别统计信息，我们还提出了两种有效的高优先指导模式预测方法。具体而言，使用超级优先信息作为输入，我们提出了两个模式预测网络，以分别预测最佳的块分辨率，以进行更好的运动编码，并决定是否从每个块中跳过剩余信息以进行更好的剩余编码而不引入额外的位成本，同时带来可忽略的额外计算成本。全面的实验结果表明，配备了新的高位指导模式预测方法，我们提出的C2F视频压缩框架实现了HEVC，UVG和MCL-JCV数据集的最新性能。
The previous deep video compression approaches only use the single scale motion compensation strategy and rarely adopt the mode prediction technique from the traditional standards like H.264/H.265 for both motion and residual compression. In this work, we first propose a coarse-to-fine (C2F) deep video compression framework for better motion compensation, in which we perform motion estimation, compression and compensation twice in a coarse to fine manner. Our C2F framework can achieve better motion compensation results without significantly increasing bit costs. Observing hyperprior information (i.e., the mean and variance values) from the hyperprior networks contains discriminant statistical information of different patches, we also propose two efficient hyperprior-guided mode prediction methods. Specifically, using hyperprior information as the input, we propose two mode prediction networks to respectively predict the optimal block resolutions for better motion coding and decide whether to skip residual information from each block for better residual coding without introducing additional bit cost while bringing negligible extra computation cost. Comprehensive experimental results demonstrate our proposed C2F video compression framework equipped with the new hyperprior-guided mode prediction methods achieves the state-of-the-art performance on HEVC, UVG and MCL-JCV datasets.
