The Implementation, in Matlab, of Three Digital Image Processing Algorithms to Evaluate the Change of Parameters of Two Consecutive Images
Klíčová slova:
MOS, PSNR, SSIM, development, Matlab, splineAbstrakt
The assessment of quality of the image plays an important role in many processing applications developments. A great effort has been carried out in recent years in this field to develop and implement image quality metrics that correlate with measures of the quality expected. In fact, a great success this domain is not achieved. In this paper we have implemented three algorithms in Matlab that evaluate two consecutive images. These algorithms are: Mean Opinion Score (MOS), Peak Signal to Noise Ratio (PSNR) and Structural Similarity Index (SSIM).Currently, we have considered 50 pairs of consecutive images retrieved from a video with a difference of 1 ms, which will be subject of entry in these three algorithms developed in Matlab. After we enter these couples of images, three numbers will appear which define the level of changing the two images parameters. The results generated will be given through statistical conclusions of spline interpolation fitting. Finally, the limitations of this study concentrate on the development and improvement of these three algorithms in such a way that the evaluation must be done as correctly as possible in cases where the difference in pixels between the two images is larger.
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Reference
Akansu, A.N., Haddad, R.A. (1992), “Multiresolution Signal Decomposition: Transforms, sub bands, wavelets”. Academic Press, San Diego CA. A modern classic that presents, among other things, some of the underlying theoretical aspects of wavelet analysis.
Aldroubi A., Unser, M. (1996), “Wavelets in Medicine and Biology”, CRC Press, Boca Raton, FL. Presents a variety of applications of wavelet analysis to biomedical engineering.
Boashash, B. (1992), “Time-Frequency Signal Analysis”, Longman Cheshire Pty Ltd. Early chapters provide a very useful introduction to time–frequency analysis followed by a number of medical applications.
Boashash, B., Black, P.J. (1987), “An efficient real-time implementation of the Wigner-Ville Distribution”, IEEE Trans. Acoust. Speech Sig. Proc. ASSP-35:1611–1618. Practical information on calculating the Wigner-Ville distribution.
Bruce, E. N. (2001),“Biomedical Signal Processing and Signal Modelling”, John Wiley and Sons, New York. Rigorous treatment with more of an emphasis on linear systems than signal processing. Introduces nonlinear concepts such as chaos.
Cichicki, A., Amari S. (2002), “Adaptive Blind Signal and Image Processing: Learning Algorithms and Applications”, John Wiley and Sons, Inc. New York. Rigorous, somewhat dense, treatment of a wide range of principal component and independent component approaches. Includes disk.
Cohen, L. (1989),“Time-frequency distributions - A review”. Proc. IEEE 77:941–981. Classic review article on the various time-frequency methods in Cohen’s class of time–frequency distributions.
Elance. Freelancer Website. Main page. Available at:www.elance.com (15/5/2016)
Ferrara, E., Widrow, B. (1992), “Fetal Electrocardiogram enhancement by time-sequenced adaptive filtering”. IEEE Trans. Biomed. Engr. BME-29:458–459. Early application of adaptive noise cancellation to a biomedical engineering problem by one of the founders of the field.