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Proceedings Paper

Sharpness metric for no-reference image visual quality assessment
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Paper Abstract

This paper presents a novel sharpness metric for color images. The proposed metric can be used for no-reference assessment of image visual quality. The metric basically relies on local power of wavelet transform high-frequency coefficients. It also takes into account possibility of presence of macrophotography and portrait photography effects in an image where the image part (usually central one) in sharp whilst the remained part (background) is smeared. Such effects usually increase subjective evaluation of image visual quality by humans. The effects are taken into consideration by joint analysis of wavelet coefficients with largest and smallest squared absolute values. Besides, we propose a simple mechanism for blocking artifact accounting (if an image is compressed by JPEG) and compensation of this factor contribution. Finally, the proposed sharpness metric is calculated in color space YCbCr as a weighted sum of sharpness components. Weight optimization has shown that a weight for intensity component Y is to be considerably smaller than weights for color components Cb and Cr. Optimization of weights for all stages of sharpness metric calculation is carried out for specialized database NRTID that contains 500 test images with previously determined MOS (Mean Opinion Score). Spearman rank order correlation coefficient (SROCC) determined for the designed sharpness metric and MOS is used as optimization criterion. After optimization, it reaches 0.71. This is larger than for other known available no-reference metrics considered at verification stage.

Paper Details

Date Published: 2 February 2012
PDF: 11 pages
Proc. SPIE 8295, Image Processing: Algorithms and Systems X; and Parallel Processing for Imaging Applications II, 829519 (2 February 2012); doi: 10.1117/12.906393
Show Author Affiliations
Nikolay N. Ponomarenko, National Aerospace Univ. (Ukraine)
Vladimir V. Lukin, National Aerospace Univ. (Ukraine)
Oleg I. Eremeev, National Aerospace Univ. (Ukraine)
Karen O. Egiazarian, Tampere Univ. of Technology (Finland)
Jaakko T. Astola, Tampere Univ. of Technology (Finland)


Published in SPIE Proceedings Vol. 8295:
Image Processing: Algorithms and Systems X; and Parallel Processing for Imaging Applications II
Karen O. Egiazarian; John Recker; Guijin Wang; Sos S. Agaian; Atanas P. Gotchev, Editor(s)

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