Journal
IEEE SIGNAL PROCESSING LETTERS
Volume 19, Issue 2, Pages 75-78Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/LSP.2011.2179293
Keywords
Distortions; image quality; local artifact; pLSA; topic model
Categories
Funding
- NSF [IIS-1116656]
- Intel under the VAWN
- Cisco under the VAWN
- Direct For Computer & Info Scie & Enginr
- Div Of Information & Intelligent Systems [1116656] Funding Source: National Science Foundation
- Division Of Computer and Network Systems
- Direct For Computer & Info Scie & Enginr [0854904] Funding Source: National Science Foundation
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We propose a highly unsupervised, training free, no reference image quality assessment (IQA) model that is based on the hypothesis that distorted images have certain latent characteristics that differ from those of natural or pristine images. These latent characteristics are uncovered by applying a topic model to visual words extracted from an assortment of pristine and distorted images. For the latent characteristics to be discriminatory between pristine and distorted images, the choice of the visual words is important. We extract quality-aware visual words that are based on natural scene statistic features [1]. We show that the similarity between the probability of occurrence of the different topics in an unseen image and the distribution of latent topics averaged over a large number of pristine natural images yields a quality measure. This measure correlates well with human difference mean opinion scores on the LIVE IQA database [2].
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