FS-BAND: A Frequency-Sensitive Banding Detector
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Abstract (arXiv)
Banding artifact, as known as staircase-like contour, is a common quality annoyance that happens in compression, transmission, etc. scenarios, which largely affects the user's quality of experience (QoE). The banding distortion typically appears as relatively small pixel-wise variations in smooth backgrounds, which is difficult to analyze in the spatial domain but easily reflected in the frequency domain. In this paper, we thereby study the banding artifact from the frequency aspect and propose a no-reference banding detection model to capture and evaluate banding artifacts, called the Frequency-Sensitive BANding Detector (FS-BAND). The proposed detector is able to generate a pixel-wise banding map with a perception correlated quality score. Experimental results show that the proposed FS-BAND method outperforms state-of-the-art image quality assessment (IQA) approaches with higher accuracy in banding classification task.
Key facts. Cited by 3 papers in the map (data through 14 Oct 2026). Builds on 10 of its 34 references that are in the map. First posted on arXiv 30 Nov 2023.
Builds on
10 of 34 references.
References · 10
- 2015Deep Residual Learning for Image Recognitioncited by 52,096
- 2018The Unreasonable Effectiveness of Deep Features as a Perceptual Metriccited by 11,177
- 2013Visualizing and Understanding Convolutional Networkscited by 2,290
- 2017NIMA: Neural Image Assessmentcited by 389
- 2019Blind Image Quality Assessment Using A Deep Bilinear Convolutional Neural Networkcited by 302
- 2016HyperFace: A Deep Multi-task Learning Framework for Face Detection, Landmark Localization, Pose Estimation, and Gender Recognitioncited by 104
- 2021Blind Quality Assessment for in-the-Wild Images via Hierarchical Feature Fusion and Iterative Mixed Database Trainingcited by 71
- 2020BBAND Index: A No-Reference Banding Artifact Predictorcited by 19
- 2021CAMBI: Contrast-aware Multiscale Banding Indexcited by 9
- 2022Banding vs. Quality: Perceptual Impact and Objective Assessmentcited by 4
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Citing papers per year: 2024: 1; 2025: 2.
3 papers.