In-loop filtering has emerged as an essential coding tool since H.264/AVC, due to its delicate design, which reduces different kinds of compression artifacts. However, existing in-loop filters rely only on local image correlations, largely ignoring nonlocal similarities. In this article, the authors explore the design philosophy of in-loop filters and discuss their vision for the future of in-loop filter research by examining the potential of nonlocal similarities. Specifically, the group-based sparse representation, which jointly exploits an image's local and nonlocal self-similarities, lays a novel and meaningful groundwork for in-loop filter design. Hard- and soft-thresholding filtering operations are applied to derive the sparse parameters that are appropriate for compression artifact reduction. Experimental results show that this in-loop filter design can significantly improve the compression performance of the High Efficiency Video Coding (HEVC) standard, leading us in a new direction for improving compression efficiency.
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Ma et al. (2016) studied this question.
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