Abstract
This paper presents a new comparative objective method for image quality evaluation. This method relies on two keys points: a local objective evaluation and a perceptual gathering. The local evaluation concerns the dissimilarities between the degraded image and the reference image; it is based on a gray-level local Hausdorff distance. This local Hausdorff distance uses a generalized distance transform which is studied here. The evaluation result is a local dissimilarity map (LDMap). In order to include perceptual information, a perceptual map based on the image properties is then proposed. The coefficients of this map are used to weight and to gather the LDMap measures into a single quality measure. The perceptual map is tunable and it gives encouraging quality measures even with naive parameters.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Baudrier, E., Morain-Nicolier, F., Millon, G., Ruan, S.: Binary-image comparison with local-dissimilarity quantification. Pattern Recognition 41(5), 1461–1478 (2008)
Levi, G., Montanari, U.: A grey-weighted skeleton. Inform. Control 17, 62–91 (1970)
Toivanen, P.: New geodesic distance transforms for gray scale images. Pattern Recognition Letters 17, 437–450 (1996)
Arlandis, J., Pérez, J.C.: The continuos distance transformation: A generalization of the distance transformation for continuos-valued images. In: Amsterdam, I. (ed.) Pattern Recognition & Applications (2000)
Toivanen, P., Elmongui, H.: Sequential local transform algorithms for gray-level distance transforms. In: Proc. of the 9th Eur. Sig. Proc. Conf. (1998)
Rutovitz, D.: Data structures for operations on digital images. In: Cheng, G.C., Ledley, R.S., Pollok, D.K., Rosenfeld, A. (eds.) Pictorial Pattern Recognition, pp. 105–133 (1968)
Lorenzetto, G.P.: Image comparison metrics: A review, July 25 (1998)
Wang, Z., Bovik, A.C.: A universal image quality index. IEEE Signal Processing Letters 9(3), 81–84 (2002)
Dinet, E., Bartholin, A.: A spatio-colorimetric model of visual attention. In: Proc. of the Expert Symp. on Visual Appearance, Paris, CIE, October 2006, pp. 97–105 (2006)
Sheikh, H.R., Wang, Z., Cormack, L., Bovik, A.C.: Live image quality assessment database release, Technical report, University of Texas (2005), http://live.ece.utexas.edu/research/quality
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2010 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Girard, N., Ogier, JM., Baudrier, É. (2010). A New Image Quality Measure Considering Perceptual Information and Local Spatial Feature. In: Ogier, JM., Liu, W., Lladós, J. (eds) Graphics Recognition. Achievements, Challenges, and Evolution. GREC 2009. Lecture Notes in Computer Science, vol 6020. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-13728-0_22
Download citation
DOI: https://doi.org/10.1007/978-3-642-13728-0_22
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-13727-3
Online ISBN: 978-3-642-13728-0
eBook Packages: Computer ScienceComputer Science (R0)