The DOI matches its Crossref record; 14 of the 14 references it cites were found in a registry or an index.
A no-reference perceptual image sharpness metric based on a cumulative probability of blur detection
Abstract
In this paper, a no-reference objective sharpness metric based on a cumulative probability of blur detection is proposed. The metric is evaluated by taking into account the human visual system (HVS) response to blur distortions. The perceptual significance of the metric is validated through subjective experiments. It is shown that the proposed metric results in a very good correlation with subjective scores especially for images with varying foreground and background perceived blur qualities. This is accomplished with a significantly lower computational complexity as compared to existing methods that take into account the visual attention information.
Overview
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
In the citing paper
“Cumulative Probability Blur Detection (CPBD) [119], a non-reference measure, is also widely used to evaluate the loss of sharpness during video generation.”
From Deep learning for visual speech analysis: A survey (2024) · cites this paper as [119]
Citations
Cites
A model of saliency-based visual attention for rapid scene analysis
Itti, L., Christof Koch, A., & Niebur, E. (1998) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/34.730558 · Cited by 11,289
Modeling attention to salient proto-objects
Walther, D. B., & Koch, C. (2006) · Neural Networks · doi:10.1016/j.neunet.2006.10.001 · Cited by 1,256
A no-reference objective image sharpness metric based on the notion of just noticeable blur (JNB)
Ferzli, R., & Karam, L. J. (2009) · IEEE Transactions on Image Processing · doi:10.1109/tip.2008.2011760 · Cited by 758
Self-citationNo-reference quality assessment using natural scene statistics: JPEG2000
Sheikh, H. R., Bovik, A. C., & Cormack, L. (2005) · IEEE Transactions on Image Processing · doi:10.1109/tip.2005.854492 · Cited by 575
A no-reference perceptual blur metric
Marziliano, P., Frederic Dufaux, A., WINKLER, S. S., & Ebrahimi, T. (2003) · Proceedings - International Conference on Image Processing · doi:10.1109/icip.2002.1038902 · Cited by 568
Digital Video Quality: Vision Models and Metrics
WINKLER, S. S. (2005) · Cited by 388
Objective video quality assessment
(2003) · Handbook of Video Databases · doi:10.1201/9780203489864-45 · Cited by 359
WINKLER, S. S. (2005) · Wiley eBooks · doi:10.1002/9780470024065 · Cited by 331
A survey of hybrid MC/DPCM/DCT video coding distortions
Yuen, M., & Wu, H. R. (1998) · Signal Processing · doi:10.1016/s0165-1684(98)00128-5 · Cited by 254
No-reference sharpness metric based on local edge kurtosis
Caviedes, J. E., & Gurbuz, S. (2003) · Proceedings - International Conference on Image Processing · doi:10.1109/icip.2002.1038901 · Cited by 141
Research works printed but not linked: 2
- 2000, Final report from the video quality experts group on the validation of objective models of video quality assessment
- sheikh, 2003, LIVE Image Quality Assessment Database
Cited by
Sorted by citations
Items 201–208 of 208
Speech-driven talking face video generation
Wei, Z., Zhuoran, T., & Qihan, Y. (2025) · Multimedia Systems · doi:10.1007/s00530-025-02022-x
VQ-VAE Based Audio-Driven Talking Face Generation from a Single Image
Li, Y., & Shen, X. (2025) · International Conference on Artificial Intelligence, Virtual Reality and Visualization (AIVRV) · doi:10.1109/aivrv67401.2025.11350858
Li, J. (2026) · International Conference on Image, Signal Processing and Pattern Recognition (ISPP) · doi:10.1109/ispp69262.2026.11543035
Controllable and temporally stable audio-driven talking-head generation for multi-user interactions
Yang, M., & Yang, H. (2026) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2026.115528
Mathematical Models for Quality Analysis of Mobile Video
Zhao, S.-Q., Jiang, H., Liang, C., Sherif, S., & Tarraf, A. (2016) · International Journal of Numerical Analysis and Modeling · doi:10.4208/ijnam.v13.n6.p879
Deng, X., Li, C., Xu, H., Gu, H., Li, W., & Zhu, X. (2026) · Electronics · doi:10.3390/electronics15204594
Metadata
- Authors
- Narvekar, N. D., & Karam, L. J.
- Affiliations
- Arizona State University (US)
- ORCID
- Karam, Lina J.
- Venue
- 2009 International Workshop on Quality of Multimedia Experience
- Publisher
- IEEE
- Published
- 2009-07
- Pages
- 87-91
- OpenAlex
- W2162220380
- Cited by
- 208
- References
- 14
- Citations by year
- 2026: 4 · 2025: 9 · 2024: 15 · 2023: 26 · 2022: 17 · 2021: 14 · 2020: 20 · 2019: 12 · 2018: 9 · 2017: 11 · 2016: 14 · 2015: 12 · 2014: 13 · 2013: 12 · 2012: 11
- FWCI
- 5.44
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- International Conference on Quality of Multimedia Experience (QoMEX) (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 › Advanced Image Processing Techniques
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Image Retrieval and Classification Techniques
- Keywords
- no-reference image quality assessment, blur detection, cumulative probability, human visual system, perceptual image quality, visual attention, computational complexity
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
