Nonlocal Adaptive In-Loop Filter via Content-Dependent Soft-Thresholding for HEVC

In-loop filters have been widely utilized in latest video coding standards to improve the video coding efficiency by reducing compression artifacts. However, existing in-loop filters only utilize image local correlations, leading to limited performance improvement. In this paper, we explore a novel adaptive in-loop filter by means of the nonlocal similar content to improve the quality of reconstructed video frames. In our proposed filter, the input video frame is first divided into different image patch groups based on their similarity, and then a soft-thresholding method is applied to the singular values of matrices composed of image patches in every group. Since compression noise is highly correlated with image content, we propose a group-wise threshold estimation method based on image statistical characteristics, coding modes and quantization parameters. To ensure the filtering efficiency, slice level control flags are utilized and determined based on the distortion changes after filtering. The proposed in-loop filter is integrated into HM7.0, and experimental results show that it can significantly improve the performance of HEVC on top of the state-of-the-art in-loop filters.