Adaptive Debanding Filter
It addresses a common but understudied visual distortion problem for video and image enhancement, though it appears incremental as it builds on existing false contour removal techniques.
The paper tackled banding artifacts in images and videos by developing an adaptive debanding filter that uses content-adaptive smoothing and dithered quantization, resulting in improved visual and quantitative performance over state-of-the-art methods.
Banding artifacts, which manifest as staircase-like color bands on pictures or video frames, is a common distortion caused by compression of low-textured smooth regions. These false contours can be very noticeable even on high-quality videos, especially when displayed on high-definition screens. Yet, relatively little attention has been applied to this problem. Here we consider banding artifact removal as a visual enhancement problem, and accordingly, we solve it by applying a form of content-adaptive smoothing filtering followed by dithered quantization, as a post-processing module. The proposed debanding filter is able to adaptively smooth banded regions while preserving image edges and details, yielding perceptually enhanced gradient rendering with limited bit-depths. Experimental results show that our proposed debanding filter outperforms state-of-the-art false contour removing algorithms both visually and quantitatively.