Low-Rank-Based Nonlocal Adaptive Loop Filter for High-Efficiency Video Compression
In video coding, the in-loop filtering has emerged as a key module due to its significant improvement on compression performance since H.264/Advanced Video Coding. Existing incorporated in-loop filters in video coding standards mainly take advantage of the local smoothness prior model used for images. In this paper, we propose a novel adaptive loop filter utilizing image nonlocal prior knowledge by imposing the low-rank constraint on similar image patches for compression noise reduction. In the filtering process, the reconstructed frame is first divided into image patch groups according to image patch similarity. The proposed in-loop filtering is formulated as an optimization problem with low-rank constraint for every group of image patches independently. It can be efficiently solved by soft-thresholding singular values of the matrix composed of image patches in the same group. To adapt the properties of the input sequences and bit budget, an adaptive threshold derivation model is established for every group of image patches according to the characteristics of compressed image patches, quantization parameters, and coding modes. Moreover, frame-level and largest coding unit-level control flags are signaled to further improve the adaptability from the sense of rate-distortion optimization. The performance of the proposed in-loop filter is analyzed when it collaborates with the existing in-loop filters in High Efficiency Video Coding. Extensive experimental results show that our proposed in-loop filter can further improve the performance of state-of-the-art video coding standard significantly, with up to 16% bit-rate savings.