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No-reference blur assessment using multi-scale gradient
M.J. Chen and A.C. Bovik
First International Workshop on Quality of Multimedia Experience
Abstract
The increasing number of demanding consumer video
applications, as exemplified by cell phone and other lowcost
digital cameras, has boosted interest in no-reference
objective image and video quality assessment (QA). In this
paper, we focus on no-reference image and video blur
assessment. There already exist a number of no-reference
blur metrics, but most are based on evaluating the widths of
intensity edges, which may not reflect real image quality in
many circumstances. Instead, we consider natural scenes
statistics and adopt multi-resolution decomposition methods
to extract reliable features for QA. First, a probabilistic
support vector machine (SVM) is applied as a rough image
quality evaluator; then the detail image is used to refine and
form the final blur metric. The algorithm is tested on the
LIVE Image Quality Database; the results show the
algorithm has high correlation with human judgment in
assessing blur distortion of images.
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