论文标题
基于神经网络的双培养基融合
Neural Network based Inter bi-prediction Blending
论文作者
论文摘要
本文提出了一种基于学习的方法，以改善视频编码中的差异预测。在常规的视频编码解决方案中，来自已经解码的参考图片的块的运动补偿是用于预测当前帧的主要工具。尤其是，通过平均两个不同运动补偿预测块获得的双预测，可以显着提高最终时间预测准确性。在这种情况下，我们引入了一个简单的神经网络，该网络进一步改善了混合操作。在网络大小和编码器模式选择方面，复杂性平衡都已进行。在最近标准化的VVC编解码器之上进行了广泛的测试，并显示出少于10K参数的网络大小的随机访问配置中的BD率提高-1.4％。我们还提出了一个简单的基于CPU的实施和直接网络量化，以评估常规编解码器框架中的复杂性/获得权衡。
This paper presents a learning-based method to improve bi-prediction in video coding. In conventional video coding solutions, the motion compensation of blocks from already decoded reference pictures stands out as the principal tool used to predict the current frame. Especially, the bi-prediction, in which a block is obtained by averaging two different motion-compensated prediction blocks, significantly improves the final temporal prediction accuracy. In this context, we introduce a simple neural network that further improves the blending operation. A complexity balance, both in terms of network size and encoder mode selection, is carried out. Extensive tests on top of the recently standardized VVC codec are performed and show a BD-rate improvement of -1.4% in random access configuration for a network size of fewer than 10k parameters. We also propose a simple CPU-based implementation and direct network quantization to assess the complexity/gains tradeoff in a conventional codec framework.
