A Deep Learning Approach for Multi-Frame In-Loop Filter of HEVC

• Published in: IEEE transactions on image processing Vol. 28; no. 11; pp. 5663 - 5678
• Main Authors: Li, Tianyi, Xu, Mai, Zhu, Ce, Yang, Ren, Wang, Zulin, Guan, Zhenyu
• Format: Journal Article
• Language: English
• Published: United States IEEE 01-11-2019; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Artificial neural networks; Coding; Deep learning; Efficiency; Encoding; Frames (data processing); High efficiency video coding; Image coding; in-loop filter; Learning systems; multiple frames; Radio frequency; Spatial data; Video coding; Video compression
• ISSN: 1057-7149; 1941-0042
• DOI: 10.1109/TIP.2019.2921877

An extensive study on the in-loop filter has been proposed for a high efficiency video coding (HEVC) standard to reduce compression artifacts, thus improving coding efficiency. However, in the existing approaches, the in-loop filter is always applied to each single frame, without exploiting the content correlation among multiple frames. In this paper, we propose a multi-frame in-loop filter (MIF) for HEVC, which enhances the visual quality of each encoded frame by leveraging its adjacent frames. Specifically, we first construct a large-scale database containing encoded frames and their corresponding raw frames of a variety of content, which can be used to learn the in-loop filter in HEVC. Furthermore, we find that there usually exist a number of reference frames of higher quality and of similar content for an encoded frame. Accordingly, a reference frame selector (RFS) is designed to identify these frames. Then, a deep neural network for MIF (known as 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 its improved generalization capacity and computational efficiency. In addition, a novel block-adaptive convolutional layer is designed and applied in the MIF-Net, for handling the artifacts influenced by coding tree unit (CTU) structure in HEVC. Extensive experiments show that our MIF approach achieves on average 11.621% saving of the Bjøntegaard delta bit-rate (BD-BR) on the standard test set, significantly outperforming the standard in-loop filter in HEVC and other state-of-the-art approaches.