NDLI: PCANet for Blind Image Quality Assessment

Content Provider	IEEE Xplore Digital Library
Author	Huizhen Jia Quansen Sun Tonghan Wang
Copyright Year	2015
Description	Author affiliation: Sch. of Comput. Sci. & Eng., Nanjing Univ. of Sci. & Technol., Nanjing, China (Huizhen Jia; Quansen Sun) \|\| Sch. of Comput. Sci. & Eng., Southeast Univ., Nanjing, China (Tonghan Wang)
Abstract	In this work, we introduce a simple deep learning network, namely, PCANet to general-purpose blind/no-reference image quality assessment (NR-IQA). The goal of no-reference/blind image quality assessment (NR-IQA) is to devise a perceptual model that can accurately predict the quality of a distorted image as human opinions, in which feature extraction is an important issue. However, for most NR-IQA models, their features extraction process were some kind of supervised models and the features are usually natural scene statistics (NSS) based or are perceptually relevant, therefore the performance of these models is limited. In this paper, we present a new NR-IQA metric in which the features are extracted unsupervisely. Once the parameters have been given to the trained deep network, it outputs the final result without any manual mending. Experimental results on the LIVE dataset show that this approach yields state-of-the-art performance.
Starting Page	195
Ending Page	198
File Size	487876
Page Count	4
File Format	PDF
e-ISBN	9781467386609
DOI	10.1109/CIS.2015.55
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2015-12-19
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Image quality Measurement deep learning Nonlinear distortion Feature extraction image quality assessment no reference Discrete cosine transforms PCANet Principal component analysis
Content Type	Text
Resource Type	Article

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4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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