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High efficiency video coding (HEVC) has brought outperforming efficiency forvideo compression. To reduce the compression artifacts of HEVC, we propose aDenseNet based approach as the in-loop filter of HEVC, which leverages multipleadjacent frames to enhance the quality of each encoded frame. Specifically, thehigher-quality frames are found by a reference frame selector (RFS). Then, adeep neural network for multi-frame in-loop filter (named MIF-Net) is developedto enhance the quality of each encoded frame by utilizing the spatialinformation of this frame and the temporal information of its neighboringhigher-quality frames. The MIF-Net is built on the recently developed DenseNet,benefiting from the improved generalization capacity and computationalefficiency. Finally, experimental results verify the effectiveness of ourmulti-frame in-loop filter, outperforming the HM baseline and otherstate-of-the-art approaches.

A DenseNet Based Approach for Multi-Frame In-Loop Filter in HEVC