A No-Reference Image Blur Metric Based on the Cumulative Probability of Blur Detection (CPBD)

This paper presents a no-reference image blur metric that is based on the study of human blur perception for varying contrast values. The metric utilizes a probabilistic model to estimate the probability of detecting blur at each edge in the image, and then the information is pooled by computing the...

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Bibliographic Details
Published in:IEEE transactions on image processing 2011-09, Vol.20 (9), p.2678-2683
Main Authors: Narvekar, N. D., Karam, L. J.
Format: Article
Language:English
Subjects:
Applied sciences
Blur detection
blur metric
Detection, estimation, filtering, equalization, prediction
Estimates
Exact sciences and technology
Human
Image coding
Image contrast
Image edge detection
Image processing
Information, signal and communications theory
Measurement
no-reference
objective
Perception
perceptual
Pixel
Probability theory
Sharpness
sharpness metric
Signal and communications theory
Signal processing
Signal, noise
Telecommunications and information theory
Transform coding
visual quality
Visualization
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Summary:This paper presents a no-reference image blur metric that is based on the study of human blur perception for varying contrast values. The metric utilizes a probabilistic model to estimate the probability of detecting blur at each edge in the image, and then the information is pooled by computing the cumulative probability of blur detection (CPBD). The performance of the metric is demonstated by comparing it with existing no-reference sharpness/blurriness metrics for various publicly available image databases.
ISSN:1057-7149
1941-0042
DOI:10.1109/TIP.2011.2131660