Abstract
In video applications, real-time image scaling techniques are often required. In this paper, an efficient implementation of a scaling engine based on 4×4 cubic convolution is proposed. The cubic convolution has a better performance than other traditional interpolation kernels and can also be realized on hardware. The engine is designed to perform arbitrary scaling ratios with an image resolution smaller than 2560×1920 pixels and can scale up or down, in horizontal or vertical direction. It is composed of four functional units and five line buffers, which makes it more competitive than conventional architectures. A strict fixed-point strategy is applied to minimize the quantization errors of hardware realization. Experimental results show that the engine provides a better image quality and a comparatively lower hardware cost than reference implementations.
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Aho, E., Vanne, J., Hamalainen, T.D., Kuusilinna, K., 2007. Configurable implementation of parallel memory based real-time video downscaler. Microprocess. Microsyst., 31(5):283–292. [doi:10.1016/j.micpro.2006.09.003]
Arandiga, F., Donat, R., Mulet, P., 2003. Adaptive interpolation of images. Signal Process., 83(2):459–464. [doi:10.1016/S0165-1684(02)00445-0]
Chen, P.Y., Lien, C.Y., Lu, C.P., 2009. VLSI implementation of an edge-oriented image scaling processor. IEEE Trans. VLSI Syst., 17(9):1275–1284. [doi:10.1109/TVLSI.2008.2003003]
Erup, L., Gardner, F.M., Harris, R.A., 1993. Interpolation in digital modems. II. Implementation and performance. IEEE Trans. Commun., 41(6):998–1008. [doi:10.1109/26.231921]
Farrow, C.W., 1988. A Continuously Variable Digital Delay Element. IEEE Int. Symp. on Circuits and Systems, 3:2641–2645. [doi:10.1109/ISCAS.1988.15483]
Feng, T., Xie, W.L., Yang, L.X., 2001. An Architecture and Implementation of Image Scaling Conversion. 4th Int. Conf. on ASIC, p.409–410. [doi:10.1109/ICASIC.2001.982587]
Gardner, F.M., 1993. Interpolation in digital modems. I. Fundamentals. IEEE Trans. Commun., 41(3):501–507. [doi: 10.1109/26.221081]
Her, I., Yuan, C.T., 1994. Resampling on a pseudohexagonal grid. CVGIP: Graph. Models Image Process., 56(4):336–347. [doi:10.1006/cgip.1994.1030]
Hong, K.P., Paik, J.K., Kim, H.J., Lee, C.H., 1996. An edge-preserving image interpolation system for a digital camcorder. IEEE Trans. Consum. Electron., 42(3):279–284. [doi:10.1109/30.536121]
Hou, H., Andrews, H., 1978. Cubic splines for image interpolation and digital filtering. IEEE Trans. Acoust. Speech Signal Process., 26(6):508–517. [doi:10.1109/TASSP.1978.1163154]
Keys, R., 1981. Cubic convolution interpolation for digital image processing. IEEE Trans. Acoust. Speech Signal Process., 29(6):1153–1160. [doi:10.1109/TASSP.1981.1163711]
Kim, C.H., Seong, S.M., Lee, J.A., Kim, L.S., 2003. Winscale: an image-scaling algorithm using an area pixel model. IEEE Trans. Circ. Syst. Video Technol., 13(6):549–553. [doi:10.1109/TCSVT.2003.813431]
Lehmann, T., Sovakar, A., Schmitt, W., Repges, R., 1997. A comparison of similarity measures for digital subtraction radiography. Comput. Biol. Med., 27(2):151–167. [doi:10.1016/S0010-4825(97)83769-9]
Lehmann, T.M., Gonner, C., Spitzer, K., 1999. Survey: interpolation methods in medical image processing. IEEE Trans. Med. Imag., 18(11):1049–1075. [doi:10.1109/42.816070]
Li, X., Orchard, M.T., 2001. New edge-directed interpolation. IEEE Trans. Image Process., 10(10):1521–1527. [doi:10.1109/83.951537]
Lin, C.C., Sheu, M.H., Chiang, H.K., Liaw, C., Wu, Z.C., 2008. The Efficient VLSI Design of BI-CUBIC Convolution Interpolation for Digital Image Processing. IEEE Int. Symp. on Circuits and Systems, p.480–483. [doi:10.1109/ISCAS.2008.4541459]
Lin, C.C., Sheu, M.H., Liaw, C., Chiang, H.K., 2010. Fast first-order polynomials convolution interpolation for realtime digital image reconstruction. IEEE Trans. Circ. Syst. Video Technol., 20(9):1260–1264. [doi:10.1109/TCSVT.2010.2057017]
Nuno-Maganda, M.A., Arias-Estrada, M.O., 2006. Real-Time FPGA-Based Architecture for Bicubic Interpolation: an Application for Digital Image Scaling. Int. Conf. on Reconfigurable Computing and FPGAs, p.1–8. [doi:10.1109/RECONFIG.2005.34]
Parker, J.A., Kenyon, R.V., Troxel, D.E., 1983. Comparison of interpolating methods for image resampling. IEEE Trans. Med. Imag., 2(1):31–39. [doi:10.1109/TMI.1983.4307610]
Sheikh, H.R., Sabir, M.F., Bovik, A.C., 2006. A statistical evaluation of recent full reference image quality assessment algorithms. IEEE Trans. Image Process., 15(11):3440–3451. [doi:10.1109/TIP.2006.881959]
Sheikh, H.R., Wang, Z., Cormack, L., Bovik, A.C., 2010. LIVE Image Quality Assessment Database Release 2. Available from http://live.ece.utexas.edu/research/quality/subjective.htm [Accessed on Oct. 18, 2010].
Shi, H.J., Ward, R., 2002. Canny Edge Based Image Expansion. IEEE Int. Symp. on Circuits and Systems, 1:785–788. [doi:10.1109/ISCAS.2002.1009958]
Shi, J.Z., Reichenbach, S.E., 2006. Image interpolation by two-dimensional parametric cubic convolution. IEEE Trans. Image Process., 15(7):1857–1870. [doi:10.1109/TIP.2006.873429]
Wang, Z., Bovik, A.C., Sheikh, H.R., Simoncelli, E.P., 2004. Image quality assessment: from error visibility to structural similarity. IEEE Trans. Image Process., 13(4):600–612. [doi:10.1109/TIP.2003.819861]
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Project supported by the National High-Tech R & D Program (863) of China (No. 2009AA011706) and the Fundamental Research Funds for the Central Universities (No. KYJD09012)
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Wang, X., Ding, Y., Liu, My. et al. Efficient implementation of a cubic-convolution based image scaling engine. J. Zhejiang Univ. - Sci. C 12, 743–753 (2011). https://doi.org/10.1631/jzus.C1100040
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DOI: https://doi.org/10.1631/jzus.C1100040