论文标题
在视频编码中解释低复杂性的CNN学习子像素运动补偿
Interpreting CNN for Low Complexity Learned Sub-pixel Motion Compensation in Video Coding
论文作者
论文摘要
深度学习在图像和视频压缩任务中表现出了巨大的潜力。但是，它以大幅增加的编码复杂性为代价带来了储蓄，这限制了其在实际应用中实施的潜力。在本文中，提出了一种新型的基于神经网络的工具，该工具改善了分数精度运动补偿所需的参考样本的插值。与以前的努力相反，提出的方法着重于通过解释网络学到的插值过滤器来降低复杂性。当在多功能视频编码（VVC）测试模型中实现该方法时，与基线VVC相比，可以为单个序列节省多达4.5％的BD率，而与全神经网络的应用相比，学习插值的复杂性显着降低。
Deep learning has shown great potential in image and video compression tasks. However, it brings bit savings at the cost of significant increases in coding complexity, which limits its potential for implementation within practical applications. In this paper, a novel neural network-based tool is presented which improves the interpolation of reference samples needed for fractional precision motion compensation. Contrary to previous efforts, the proposed approach focuses on complexity reduction achieved by interpreting the interpolation filters learned by the networks. When the approach is implemented in the Versatile Video Coding (VVC) test model, up to 4.5% BD-rate saving for individual sequences is achieved compared with the baseline VVC, while the complexity of learned interpolation is significantly reduced compared to the application of full neural network.
