Understanding Banding - Perceptual Modelling and Machine Learning Approaches for Banding Detection

Banding is an annoying visual artifact that frequently appears at various stages along the chain of video acquisition, production, distribution and display. With the thriving popularity of ultra-high definition, high dynamic range, wide color gamut content, and the increasing user expectations that follow, the banding effect has been attracting a growing deal of attention for its strong negative impact on viewer experience in visual content that could otherwise have nearly perfect quality. Here we present two different types of frameworks to detect the banding artifact. The first is knowledge-driven and is built upon computational models that account for the characteristics of the human visual system, the content acquisition, production, distribution and display processes, and the interplay between them. The second is data-driven, and is based on machine learning methods, by training deep neural networks with large-scale datasets.

Published

2021-11

Content type

Original Research

Keywords

Banding impairment, contouring impairment, perceived video quality, human visual system

DOI

10.5594/M001952