Understanding, detecting, and removing perceptual banding artifacts in compressed videos

Banding artifacts, or false contouring, are a common compression impairment that often appears on large smooth regions of encoded videos and images. These staircase-like color bands can be very noticeable and annoying, even on otherwise high-quality videos, especially when displayed on high-definition screens. Yet, relatively little attention has been applied to this problem. Here we study this artifact, by first analyzing the perceptual and encoding aspects of banding artifacts, then propose a new distortion-specific no-reference video quality algorithm for predicting banding artifacts, inspired by perceptual models. The proposed banding detector can generate a pixel-wise banding visibility map, and output overall banding severity scores at both the frame and video levels. Furthermore, we propose a deep learning based approach to improve the overall perceptual quality of compressed videos by joint debanding and compression artifact removal. Our experimental results show that the proposed banding detector delivers better consistency with subjective evaluations, and is able to detect different perceptual severity levels of bands. The debanding experiments also show that the proposed algorithm outperforms recent debanding models both visually and quantitatively. The code is available at https://github.com/google/bband-adaband and https://github.com/vztu/DebandingNet.