Adaptive Debanding Filter

TL;DR

This paper introduces an adaptive debanding filter that effectively removes banding artifacts from images and videos by content-aware smoothing and dithering, improving visual quality over existing methods.

Contribution

It presents a novel content-adaptive smoothing and dithering approach for debanding, outperforming current algorithms in both visual quality and quantitative metrics.

Findings

Outperforms state-of-the-art debanding algorithms visually.

Provides better preservation of edges and details.

Enhances gradient rendering with limited bit-depths.

Abstract

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.