Fast and reliable structure-oriented video noise estimation
Abstract
Noise can significantly impact the effectiveness of video processing algorithms. This paper proposes a fast white-noise variance estimation that is reliable even in images with large textured areas. This method finds intensity-homogeneous blocks first and then estimates the noise variance in these blocks, taking image structure into account. This paper proposes a new measure to determine homogeneous blocks and a new structure analyzer for rejecting blocks with structure. This analyzer is based on high-pass operators and special masks for corners to stabilize the homogeneity estimation. For typical video quality (PSNR of 20-40 dB), the proposed method outperforms other methods significantly and the worst-case estimation error is 3 dB, which is suitable for real applications such as video broadcasts. The method performs well both in highly noisy and good-quality images. It also works well in images including few uniform blocks.
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Cites
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UGC-VQA: Benchmarking Blind Video Quality Assessment for User Generated Content
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- Authors
- Amer, A., & Dubois, E.
- Affiliations
- Concordia University (CA)
- ORCID
- Amer, A.
- Venue
- IEEE Transactions on Circuits and Systems for Video Technology
- Published
- 2005-01
- Volume
- 15
- Issue
- 1
- Pages
- 113-118
- ISSN
- 1051-8215 (ISSN-L)
- OpenAlex
- W4233323564
- Cited by
- 178
- References
- 8
- Citations by year
- 2026: 1 · 2025: 4 · 2024: 4 · 2023: 11 · 2022: 4 · 2021: 7 · 2020: 4 · 2019: 14 · 2018: 7 · 2017: 5 · 2016: 9 · 2015: 12 · 2014: 8 · 2013: 12 · 2012: 14
- FWCI
- 13.10
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- IEEE Transactions on Circuits and Systems for Video Technology (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Image and Video Quality Assessment
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Image and Signal Denoising Methods
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Image Enhancement Techniques
- Keywords
- image structure analysis, corner detection, textured images, video broadcasting, video quality assessment
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