论文标题
有效适应视频压缩的神经网络过滤器
Efficient Adaptation of Neural Network Filter for Video Compression
论文作者
论文摘要
我们为神经网络过滤器提供了一种有效的填充方法，该方法被用作视频编码管道中的伪影的后处理步骤。微调在编码器侧进行，以使神经网络适应正在编码的特定内容。为了最大化PSNR增益并最大程度地减少比特率开销，我们建议仅征服卷积层的偏见。所提出的方法的收敛速度比常规的鉴定方法快得多，因此它适用于实际应用。重量更高可以包含在现有视频编解码器生成的视频bitstream中。我们表明，与7个测试序列上的最先进的多功能视频编码（VVC）标准编解码器相比，我们的方法达到了9.7％的平均BD速率增益。
We present an efficient finetuning methodology for neural-network filters which are applied as a postprocessing artifact-removal step in video coding pipelines. The fine-tuning is performed at encoder side to adapt the neural network to the specific content that is being encoded. In order to maximize the PSNR gain and minimize the bitrate overhead, we propose to finetune only the convolutional layers' biases. The proposed method achieves convergence much faster than conventional finetuning approaches, making it suitable for practical applications. The weight-update can be included into the video bitstream generated by the existing video codecs. We show that our method achieves up to 9.7% average BD-rate gain when compared to the state-of-art Versatile Video Coding (VVC) standard codec on 7 test sequences.
