NDLI: A Novel Probabilistic Latent Semantic Analysis Based Image Blur Metric

Content Provider	IEEE Xplore Digital Library
Author	Zhang Tao Zhang Qi Liang Dequn
Copyright Year	2014
Description	Author affiliation: Sch. of Inf. Sci. & Technol., Dalian Maritime Univ., Dalian, China (Zhang Tao; Zhang Qi; Liang Dequn)
Abstract	The proposed metric is to use latent quality aware topics in an image to measure blurriness. A novel image quality vocabulary is firstly obtained by the contrast features computed from the training images using K-means. Probabilistic latent semantic analysis model is then used to discover quality aware topics that are latent in clear sample images and the test image. The similarity between the latent topics of the test image and the average topics of clear images is finally computed to measure blurriness. Experimental results show that the proposed blur metric is monotonic, robust to additive noises, and also consistent with the human visual system.
Sponsorship	IEEE Comput. Soc.
Starting Page	310
Ending Page	315
File Size	522488
Page Count	6
File Format	PDF
e-ISBN	9781479950799
DOI	10.1109/DASC.2014.62
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2014-08-24
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Measurement Training Image quality Vocabulary Visualization human visual system Semantics probabilistic latent semantic analysis Feature extraction image quality assessment blur metric no-reference
Content Type	Text
Resource Type	Article

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