NDLI: A novel image quality assessment metric using singular value decomposition

Content Provider	IEEE Xplore Digital Library
Author	Ali, S.S.
Copyright Year	2015
Abstract	Image quality assessment (IQA) plays an important role in many applications such as image compression and transmission. In this paper a full referenced IQA (FR-IQA) model has been proposed which is based upon transformation based technique. Singular value decomposition (SVD) has been used to determine the basis vectors that best describe the input image signal. In contrast to other transformation based techniques such as discrete cosine transformation (DCT) and wavelet transform (WT), SVD does not use predefined basis vectors. In this paper a new methodology has been adopted in which both reference and distorted images are first combined together and then SVD is applied to compute the basis vectors. Projection coefficients of both reference and distorted images when projected onto these basis vectors have been used to calculate the final score. The proposed methodology has been tested on three publicly available image databases. The results of proposed methodology are better than most of the state of the art IQA metrics.
Starting Page	170
Ending Page	173
File Size	406272
Page Count	4
File Format	PDF
e-ISBN	9781479965601
DOI	10.1109/CWIT.2015.7255178
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2015-07-06
Publisher Place	Canada
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Computational modeling Image quality Distortion Measurement Transform coding Conferences CSIQ image database Image quality assessment (IQA) Singular value decomposition (SVD) TID2008
Content Type	Text
Resource Type	Article

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