Efficient Adaptation of Neural Network Filter for Video Compression

• URL: http://arxiv.org/abs/2007.14267v2
• Date: Thu, 13 Aug 2020 09:07:25 GMT
• Title: Efficient Adaptation of Neural Network Filter for Video Compression
• Authors: Yat-Hong Lam, Alireza Zare, Francesco Cricri, Jani Lainema, Miska Hannuksela
• Abstract summary: We present an efficient finetuning methodology for neural-network filters. The fine-tuning is performed at encoder side to adapt the neural network to the specific content that is being encoded. The proposed method achieves much faster than conventional finetuning approaches.
• Score: 10.769305738505071
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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