Deformable Wiener Filter for Future Video Coding
2026-06-01 • Computer Vision and Pattern Recognition
Computer Vision and Pattern Recognition
AI summaryⓘ
The authors focus on improving in-loop filters, which help reduce noise in video compression. They note that current filters mainly use information from nearby pixels, missing out on similar patterns that appear elsewhere. To fix this, they created a new filter called the deformable Wiener Filter (DWF) that learns how to combine both local and non-local information better through supervised training. Their method also groups similar pixels to apply customized filtering, leading to better video compression efficiency. Tests show that their approach saves bit rate compared to existing methods in different video coding scenarios.
in-loop filterVersatile Video Coding (VVC)noise reductionWiener Filterlocal similaritynon-local similaritysupervised trainingbit-rate savingsvideo compressionfilter coefficients
Authors
Xuewei Meng, Chuanmin Jia, Xinfeng Zhang, Shanshe Wang, Siwei Ma
Abstract
In-loop filters have attracted increasing attention due to the remarkable noise-reduction capability in the hybrid video coding framework. However, the existing in-loop filters in Versatile Video Coding (VVC) mainly take advantage of the image local similarity. Although some non-local based in-loop filters can make up for this shortcoming, the widely-used unsupervised parameter estimation method by non-local filters limits the performance. In view of this, we propose a deformable Wiener Filter (DWF). It combines the local and non-local characteristics and supervisedly trains the filter coefficients based on the Wiener Filter theory. In the filtering process, local adjacent samples and non-local similar samples are first derived for each sample of interest. Then the to-be-filtered samples are classified into specific groups based on the patch level noise and sample-level characteristics. Samples in each group share the same filter coefficients. After that, the local and non-local reference samples are adaptively fused based on the classification results. Finally, the filtering operation with outlier data constraints is conducted for each to-be-filtered sample. Moreover, the performance of the proposed DWF is analyzed with different reference sample derivation schemes in detail. Simulation results show that the proposed approach achieves 1.16%, 1.92%, and 2.67% bit-rate savings on average compared to the VTM-11.0 for All Intra, Random Access, and Low-Delay B configurations, respectively.