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Proceedings Paper

Efficiency analysis of DCT-based filters for color image database
Author(s): Dmitriy V. Fevralev; Nikolay N. Ponomarenko; Vladimir V. Lukin; Sergey K. Abramov; Karen O. Egiazarian; Jaakko T. Astola
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Paper Abstract

Images formed by different systems are often noisy which makes filtering a typical operation of image pre-processing. In many research papers, filter performance is analyzed for a limited number of standard test images and noise variances. Here we use a recently created color image database TID2008 that allows assessing filter efficiency for 25 color images corrupted by noise with different values of variance, both i.i.d. and spatially correlated. Besides, this image database serves the purpose of evaluating different quality metrics including those able to characterize visual quality of original and processed images considerably better than conventional MSE and PSNR. The study is carried out for filters based on discrete cosine transform (DCT) able to suppress both i.i.d. and spatially correlated noise depending upon a way of threshold setting. It is shown that improvement of PSNR (IPSNR) due to filtering is very close for R, G, and B components of color images and this improvement depends on image content. IPSNR reaches 9 dB for quite simple images and it is only about 1 dB for highly textural images if initial PSNR=30 dB. Note that IPSNR is larger if the original PSNR is smaller. The visual quality metric PSNR-HVS-M is studied as well. The metric PSNR-HVS-M becomes larger due to filtering but in smaller degree than PSNR does. We demonstrate that it is possible to forecast whether or not visual quality can be improved due to filtering or to detect in advance highly textural images for which filtering can be not efficient enough. The provided output MSEs are also compared to potential limits calculated according to the recently proposed methodology. It is demonstrated that for highly textural images the DCT filtering with 8x8 full overlapping blocks and hard thresholding provides output MSE close to potential limits. The provided and limit MSEs differ from each other by about 10%. For simpler images, the provided and limit MSEs can differ by 1.5...2.5 times. Analysis is also carried out for spatially correlated noise. It is shown that efficiency of filtering in this case is lower.

Paper Details

Date Published: 4 February 2011
PDF: 12 pages
Proc. SPIE 7870, Image Processing: Algorithms and Systems IX, 78700R (4 February 2011); doi: 10.1117/12.871944
Show Author Affiliations
Dmitriy V. Fevralev, National Aerospace Univ. (Ukraine)
Nikolay N. Ponomarenko, National Aerospace Univ. (Ukraine)
Vladimir V. Lukin, National Aerospace Univ. (Ukraine)
Sergey K. Abramov, 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. 7870:
Image Processing: Algorithms and Systems IX
Jaakko T. Astola; Karen O. Egiazarian, Editor(s)

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