No-reference and robust image sharpness evaluation based on multiscale spatial and spectral features

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalpeer-review

102 Scopus Citations
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Author(s)

Detail(s)

Original languageEnglish
Article number7784707
Pages (from-to)1030-1040
Journal / PublicationIEEE Transactions on Multimedia
Volume19
Issue number5
Online published15 Dec 2016
Publication statusPublished - May 2017
Externally publishedYes

Abstract

The human visual system exhibits multiscale characteristic when perceiving visual scenes. The hierarchical structures of an image are contained in its scale space representation, in which the image can be portrayed by a series of increasingly smoothed images. Inspired by this, this paper presents a no-reference and robust image sharpness evaluation (RISE) method by learning multiscale features extracted in both the spatial and spectral domains. For an image, the scale space is first built. Then sharpness-aware features are extracted in gradient domain and singular value decomposition domain, respectively. In order to take into account the impact of viewing distance on image quality, the input image is also down-sampled by several times, and the DCT-domain entropies are calculated as quality features. Finally, all features are utilized to learn a support vector regression model for sharpness prediction. Extensive experiments are conducted on four synthetically and two real blurred image databases. The experimental results demonstrate that the proposed RISE metric is superior to the relevant state-of-the-art methods for evaluating both synthetic and real blurring. Furthermore, the proposed metric is robust, which means that it has very good generalization ability.

Research Area(s)

  • entropy, gradient, Image sharpness evaluation, scale space, singular value decomposition, support vector regression (SVR)

Citation Format(s)

No-reference and robust image sharpness evaluation based on multiscale spatial and spectral features. / Li, Leida; Xia, Wenhan; Lin, Weisi; Fang, Yuming; Wang, Shiqi.

In: IEEE Transactions on Multimedia, Vol. 19, No. 5, 7784707, 05.2017, p. 1030-1040.

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalpeer-review