Abstract
Assessing the subjective quality of processed images through an objective quality metric is a key issue in multimedia processing and transmission. In some scenarios, it is also important to evaluate the quality of the received images with minimal reference to the transmitted ones. For instance, for closed-loop optimisation of image and video transmission, the quality measure can be evaluated at the receiver and provided as feedback information to the system controller. The original images - prior to compression and transmission - are not usually available at the receiver side, and it is important to rely at the receiver side on an objective quality metric that does not need reference or needs minimal reference to the original images.
The observation that the human eye is very sensitive to edge and contour information of an image underpins the proposal of our reduced reference (RR) quality metric, which compares edge information between the distorted and the original image.
Results highlight that the metric correlates well with subjective observations, also in comparison with commonly used full-reference metrics and with a state-of-the-art reduced reference metric.
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References
Pinson, M.H., Wolf, S.: Comparing subjective video quality technologies. In: Proc. of SPIE Video Communication and Image Processing, Lugano, Switzerland (September 2003)
Eskicioglu, A.M., Fisher, P.S.: Image quality measures and their performance. IEEE Transactions on Comms. 43, 2959–2965 (1995)
Pinson, M.H., Wolf, S.: A new standardized method for objectively measuring video quality. IEEE Transactions on Broadcasting 50(3), 312–322 (2004)
Wang, Z., Bovik, A., Sheikh, H., Simoncelli, E.: Image quality assessment: from error measurement to structural similarity. IEEE Trans. Image Processing 13(4), 600–612 (2004)
Sheikh, H.R., Sabir, M., Bovik, A.C.: A statistical evaluation of recent full reference image quality assessment algorithms. IEEE Trans. Image Processing 15(11), 3440–3451 (2006)
Wang, Z., Simoncelli, E.P.: Reduced-reference image quality assessment using a wavelet-domain natural image statistic model. In: Human Vision and Electronic Imaging, pp. 149–159 (March 2005)
Martini, M.G., Mazzotti, M., Lamy-Bergot, C., Huusko, J., Amon, P.: Content adaptive network aware joint optimization of wireless video transmission. IEEE Communications Magazine 45(1), 84–90 (2007)
Marr, D., Hildreth, E.: Theory of edge detection. Proceedings of the Royal Society of London. Series B (1980)
Zhang, M., Mou, X.: A psychovisual image quality metric based on multi-scale structure similarity. In: Proc. IEEE International Conference on Image Processing (ICIP), San Diego, CA, pp. 381–384 (October 2008)
Woods, J.: Multidimensional Signal, Image and Video Processing and Coding. Elsevier (2006)
Yarbus, A.L.: Eye Movements and Vision. Plenum Press, New York (1967)
Privitera, C.M., Stark, L.W.: Algorithms for defining visual regions-of-interest: comparison with eye fixations. IEEE Trans. Pattern Anal. Mach. Intell. 22(9), 970–982 (2000)
Engelke, U., Zepernick, H.: Framework for optimal region of interest-based quality assessment in wireless imaging. Journal of Electronic Imaging 19(1), 011005–1 – 011 005–13 (2010)
Musoromy, Z., Bensaali, F., Ramalingam, S., Pissanidis, G.: Comparison of real-time DSP-based edge detection techniques for license plate detection. In: Sixth International Conference on Information Assurance and Security, Atlanta, GA, pp. 323–328 (August 2010)
Zhou, W., Xie, Z., Hua, C., Sun, C., Zhang, J.: Research on edge detection for image based on wavelet transform. In: Proceedings of the 2009 Second International Conference on Intelligent Computation Technology and Automation, Washington, DC, USA, pp. 686–689 (2009)
Kazakova, N., Margala, M., Durdle, N.G.: Sobel edge detection processor for a real-time volume rendering system. In: Proc. of the 2004 International Symposium on Circuits and Systems (ISCAS 2004), pp. 913–916 (May 2004)
Seshadrinathan, K., Soundararajan, R., Bovik, A.C., Cormack, L.K.: LIVE video quality assessment database (2010), http://live.ece.utexas.edu/research/quality/live_video.html
Sheikh, H.R., Wang, Z., Cormack, L., Bovik, A.C.: Live image quality assessment database (2008), http://live.ece.utexas.edu/research/quality
Callet, P.L., Autrusseau, F.: Subjective quality assessment IRCCyN/IVC database (2005), http://www.irccyn.ec-nantes.fr/ivcdb/
van Dijk, A.M., Martens, J.B., Watson, A.B.: Quality assessment of coded images using numerical category scaling. In: Proc. SPIE, vol. 2451, pp. 99–101 (1995)
Wang, Z., Bovik, A.C., Sheikh, H.R., Simoncelli, E.P.: The SSIM index for image quality assessment (2008), http://www.ece.uwaterloo.ca/~z70wang/research/ssim/#usage
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© 2012 ICST Institute for Computer Science, Social Informatics and Telecommunications Engineering
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Martini, M.G., Villarini, B., Fiorucci, F. (2012). Reduced-Reference Image Quality Assessment Based on Edge Preservation. In: Atzori, L., Delgado, J., Giusto, D. (eds) Mobile Multimedia Communications. MobiMedia 2011. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 79. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-30419-4_3
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DOI: https://doi.org/10.1007/978-3-642-30419-4_3
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