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

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Publisher: SMPTE — White Plains, NY, USA
Doc Type: Conference Paper
Content Type: Original Research
Volume: 2021, No. 11, pp. 1–12
Abstract: 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.
Publication Date: 2021-11-09
DOI: 10.5594/M001952
Link: https://doi.org/10.5594/M001952
Author(s): Hojatollah YeganehSSIMWAVE Inc.
Kai ZengSSIMWAVE Inc.
Zhou WangSSIMWAVE Inc.; University of Waterloo
Keyword(s): Banding, Contouring Impairment, Video Quality, Human Visual System
Copyright: © 2021 Society of Motion Picture and Television Engineers, Inc.

Bibliographic Reference(s)

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Hojatollah Yeganeh, Kai Zeng, and Zhou Wang; Understanding Banding - Perceptual Modelling and Machine Learning Approaches for Banding Detection, SMPTE Meetings and Conferences ( November 2021); SMPTE, 2021. Available at https://doi.org/10.5594/M001952

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Hojatollah Yeganeh, Kai Zeng, and Zhou Wang; Understanding Banding - Perceptual Modelling and Machine Learning Approaches for Banding Detection, SMPTE Meetings and Conferences ( November 2021); SMPTE, 2021. Available at https://doi.org/10.5594/M001952

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<span class="citation">Hojatollah Yeganeh, Kai Zeng, and Zhou Wang; <cite>Understanding Banding - Perceptual Modelling and Machine Learning Approaches for Banding Detection</cite>, SMPTE Meetings and Conferences ( November 2021); SMPTE, 2021. Available at <a href="https://doi.org/10.5594/M001952" target="_blank" rel="noopener">https://doi.org/10.5594/M001952</a></span>

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Hojatollah Yeganeh, Kai Zeng, and Zhou Wang; <cite id="bib-10-5594-m001952">Understanding Banding - Perceptual Modelling and Machine Learning Approaches for Banding Detection</cite>, SMPTE Meetings and Conferences ( November 2021); SMPTE, 2021
<span class="doi">10.5594/M001952</span>
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