Shearlet-based intensity classification loop filter for video coding

The new Versatile Video Coding standard (VVC) exhibits a significantly higher coding efficiency in comparison to its predecessor High Efficiency Video Coding (HEVC). Since the finalization of VVC in July 2020 many new in-loop compression tools were suggested, especially with data-driven approaches. The question arises if non-data-driven in-loop filtering approaches can further increase coding efficiency beyond the capability of VVC. In this paper we approach the task of filtering images corrupted by quantization noise from the perspective of applied harmonic analysis, an area of applied mathematics focusing on the efficient representation, analysis and encoding of data. The shearlet-based intensity classification loop filter (SCLF) is a novel non-data-driven approach using an overcomplete and sparsifying transform, the shearlet transform. The basic idea is to achieve a signal-noise separation by applying a shearlet transform. Shearlets can identify important structures of natural images such as edges in the sparse transform domain. Each shearlet transform coefficient is classified into different classes. All coefficients with similar intensities are grouped into the same class, which eventually gives a partition of the set of all shearlet coefficients. This separates important information from noise and ensures an accurate reconstruction of original information by performing an inverse shearlet transform combined with Wiener filtering separately for each class. SCLF effectively removes compression artifacts and therefore restores subjective visual quality. Simulation results show that by adding SCLF to the in-loop stage yet further average bit rate reductions of up to 1.5% are achieved for JVET common test sequences.