BBAND INDEX: A NO-REFERENCE BANDING ARTIFACT PREDICTOR
Open accessAbstract
Banding artifact, or false contouring, is a common video compression impairment that tends to appear on large flat regions in encoded videos. These staircase-shaped color bands can be very noticeable in high-definition videos. Here we study this artifact, and propose a new distortion-specific no-reference video quality model for predicting banding artifacts, called the Blind BANding Detector (BBAND index). BBAND is inspired by human visual models. The proposed detector can generate a pixel-wise banding visibility map and output a banding severity score at both the frame and video levels. Experimental results show that our proposed method outperforms state-of-the-art banding detection algorithms and delivers better consistency with subjective evaluations.
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- Authors
- Tu, Z., Lin, J., Wang, Y., Adsumilli, B., & Bovik, A. C.
- Affiliations
- The University of Texas at Austin (US)
- Google (United States) (US)
- Venue
- ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
- Publisher
- IEEE
- Published
- 2020-05
- Pages
- 2712-2716
- ISSN
- 1520-6149 (ISSN-L)
- OpenAlex
- W3015863973
- Cited by
- 46
- References
- 28
- Citations by year
- 2026: 1 · 2025: 5 · 2024: 12 · 2023: 2 · 2022: 11 · 2021: 10 · 2020: 5
- FWCI
- 2.16
- Type
- conference paper
- Language
- en
- Open access
- green
- arxiv.org
- Licence
- Locations
- IEEE International Conference on Acoustics Speech and Signal Processing (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Image and Video Quality Assessment
- Physical Sciences › Computer Science › Signal Processing › Video Coding and Compression Technologies
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Digital Media Forensic Detection
- Keywords
- banding artifacts, false contouring, no-reference video quality assessment, human visual model, high-definition video
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