Author:
Savić Nataša,Milivojević Zoran,Prlinčević Bojan
Abstract
The first part of the paper describes a two-parameter (2P) fifth-order interpolation kernel, r. After that, from the 2P kernel, the kernel components were created. By applying the Fourier transformation to each kernel component, the spectral components of the 2P kernel were obtained. The spectral characteristic of the 2P kernel, H, was created from the spectral components. After that, the algorithm, that optimizes the parameters of the 2P kernel so as to eliminate the ripple of the spectral characteristics, is described. The optimization was performed in such a way that the spectral characteristic developed in the Taylor series, HT. With the condition for the elimination of the members of the Tylor series, which greatly affect the ripple of the spectral characteristic, the optimal kernel parameters (aopt, bopt) were determined. The second part of the paper describes an Experiment, in which the interpolation accuracy of the 2P kernel was tested. Convolution interpolation, with the 2P kernel, was performed over the signals from the Test base. The Test base is created with musical signals. By analyzing the interpolation error, which is represented by the Mean Square Error, MSE, the precision of the interpolation was determined. The results (aopt, bopt, MSEmin) are presented on tables and graphs. Detailed comparative analysis showed higher interpolation precision with the proposed 2P interpolation kernel, compared to the interpolation precision with, 1P interpolation kernel. Finally, the numerical values of the optimal kernel parameters, which are determined by the optimization algorithm proposed in this paper, were experimentally verified.
Publisher
Centre for Evaluation in Education and Science (CEON/CEES)
Reference14 articles.
1. Deng, T.-B. 2010. Frequency-domain weighted-least-squares design of quadratic interpolators. IET Signal Processing, 4(1), 102. https://doi.org/10.1049/iet-spr.2008.0125;
2. Dodgson, N. A. 1997. Quadratic interpolation for Image Resampling. IEEE Transactions on Image Processing, 6(9), pp. 1322-1326. https://doi.org/10.1109/83.623195;
3. Goto, M., Hashiguchi, H., Nishimura, T., Oka, R. 2003. RWC Music Database:Music Genre Database and Musical Instrument Sound Database. Proceedings of the 4th International Conference on Music Information Retrieval, pp. 229-230;
4. Keys, R. 1981. Cubic convolution interpolation for digital image processing. IEEE Transactions on Acoustics, Speech, and Signal Processing, 29(6), pp. 1153-1160. https://doi.org/10.1109/tassp.1981.1163711;
5. Meijering, E. H. W., Zuiderveld, K. J., Viergever, M. A. 1999. Image reconstruction by convolution with symmetrical piecewise nth-order polynomial kernels. IEEE Transactions on Image Processing, 8(2), pp. 192-201. https://doi.org/10.1109/83.743854;