High efficiency video coding (HEVC) has brought outperforming efficiency for video compression. To reduce the compression artifacts of HEVC, we propose a DenseNet based approach as the in-loop filter of HEVC, which leverages multiple adjacent frames to enhance the quality of each encoded frame. Specifically, the higher-quality frames are found by a reference frame selector (RFS). Then, a deep neural network for multi-frame in-loop filter (named MIF-Net) is developed to enhance the quality of each encoded frame by utilizing the spatial information of this frame and the temporal information of its neighboring higher-quality frames. The MIF-Net is built on the recently developed DenseNet, benefiting from the improved generalization capacity and computational efficiency. Finally, experimental results verify the effectiveness of our multi-frame in-loop filter, outperforming the HM baseline and other state-of-the-art approaches.
@article{arxiv.1903.01648,
title = {A DenseNet Based Approach for Multi-Frame In-Loop Filter in HEVC},
author = {Tianyi Li and Mai Xu and Ren Yang and Xiaoming Tao},
journal= {arXiv preprint arXiv:1903.01648},
year = {2019}
}
Comments
10 pages, 4 figures. Accepted by Data Compression Conference 2019