Local Variance Based Color Image Quality Assessment Method

Abstract:

In this paper, local variance is used to describe the structural information of a color image in order to assess its quality. The representation method is different from conventional models in that some information that is sensitive to human eyes is enhanced by using local variance distribution. It encodes the local variance distribution of different channels of a color image into the three imaginary parts of a quaternion. The distance between the singular value feature vectors of the source image block and the distorted image block which are described by quaternion matrices is calculated. The experimental results show that the assessment results of the proposed assessment method are more consistent with the Human Visual System than those of the conventional assessment methods.

• Periodical: Advanced Materials Research (Volumes 301-303)
• Pages: 1254-1259
• DOI: https://doi.org/10.4028/www.scientific.net/AMR.301-303.1254
• Online since: July 2011
• Authors: Yu Qing Wang
• Keywords: Color Image, Quality Assessment, Quaternion, Singular Value Decomposition (SVD)

References

[1] Zhou Wang, Bovik A C, Sheikh H R, et a1. Image quality assessment: From error visibility to structural similarity,. IEEE Transactions on Image Processing, VOL. 13, no. 4, 600-612, Apr. (2004).

DOI: 10.1109/tip.2003.819861

[2] Aleksandr Shnayderman, Alexander Gusev, and Ahmet M. Eskicioglu. An SVD-Based Grayscale Image Quality Measure for Local and Global Assessment, IEEE Transactions on Image Processing, vol. 15, no. 2, 422-429, Feb. (2006).

DOI: 10.1109/tip.2005.860605

[3] S.J.P. Westen, R.L. Lagendijk, J. Biemond. Perceptual image quality based on a multiple channel HVS model,. IEEE 1995 International Conference on Acoustics, Speech, and Signal Processing, Detroit, MI, USA, (1995), 2351-2354.

DOI: 10.1109/icassp.1995.479964

[4] Mauro Barni, Franco Bartolin, Alessia De Rosa. HVS modelling for quality evaluation of art images., IEEE 14th International Conference on Digital Signal Processing, Santorini, Greece, July (2002), 91-94.

DOI: 10.1109/icdsp.2002.1027822

[5] Santiago Aja-Fernandez, Raul San Jose Estepar, Carlos Alberola-Lopez , et al. Image Quality Assessment based on Local Variance,. Proceedings of 28th IEEE EMBS Annual International Conference. New York City, USA, (2006). 4815- 4818.

DOI: 10.1109/iembs.2006.259516

[6] Pei S-C, Cheng C-M. A novel block truncation coding of color images by using quaternion moment preserving principle,. IEEE Transactions on Communications, VOL. 45, no. 5, 583-595, MAY. (1997).

DOI: 10.1109/26.592558

[7] Nicolas Le Bihan, and Stephen J. Sangwine. Quaternion principal component analysis of color images., IEEE International Conference on Image Processing(ICIP2003), Barcelona, Spain, (2003), VOL. 1, 809-812.

DOI: 10.1109/icip.2003.1247085

[8] Nicolas Le Bihan, and Stephen J. Sangwine. Color image decomposition using quaternion singular value decomposition, International Conference on Visual Information Engineering (VIE 2003). Guildford, UK, (2003)113-116.

DOI: 10.1049/cp:20030500

[9] Nicolas Le Bihan, Jerome Mars. Singular value decomposition of quaternion matrices: a new tool for vector-sensor signal processing., Signal Processing, VOL. 84, no. 7, l177-1199. (2006).

DOI: 10.1016/j.sigpro.2004.04.001

[10] Zhang F. Quaternion and matrices of quaternion., Lin. Alge. Appl, (1997), 215: 21-57.

[11] Hamilton W R. On quaternions,. Proceeding of the Royal Irish Academy, NOV. 11, 1844.

[12] J.B. Fraleigh, A First course in Abstract Algebra, Addison-Wesley, (1982).

[13] D.S. Taubman, M.W. Marcellin, JPEG2000: Image Compression Fundamentals, Standards, and Practice,. Norwell, MA: Kluwer, (2001).

DOI: 10.1007/978-1-4615-0799-4