π€ AI Summary
This work addresses the high computational complexity and excessive memory accesses associated with Adaptive Loop Filtering (ALF) in Versatile Video Coding (VVC), which hinder its efficient implementation. The authors propose an optimized ALF framework that, for the first time, enables parallel processing of Gradient-based Adaptive Loop Filtering (GALF) and Cross-Component Adaptive Loop Filtering (CCALF). The framework introduces an adaptive parameter decision mechanism for GALF and a single-pass CCALF scheme based on distortion estimation, eliminating the need for actual filtering during parameter selection. This approach reduces image buffer accesses from 152 to just one per frame and decreases ALF module runtime by approximately 25%, with negligible coding performance loss. Notably, selected components of this method have already been integrated into the VVC Test Model (VTM) reference software.
π Abstract
In the Versatile Video Coding~(VVC) standard, adaptive loop filter~(ALF), including Geometry transformation-based Adaptive Loop Filter~(GALF) and Cross Component Adaptive Loop Filter~(CCALF), plays an essential role in reducing compression artifacts. However, it also has high coding complexity and requires many picture buffer accesses in the encoder that will increase external memory access and is unfriendly to the software and hardware design. Therefore, we propose an optimized ALF framework, including the parallel design of GALF and CCALF, the adaptive parameter decision of GALF, and one-pass CCALF scheme by effectively estimating the CCALF filtering distortion without conducting filter operation. Compared to VTM-8.0, the proposed method can reduce the picture buffer access from 152 to 1 and achieve roughly 25\% time-savings of the ALF module with negligible coding performance change under RA configuration. Some of the proposed methods have been adopted in the VVC reference software.