Abstract
Video up-conversion takes significant place in various application areas. One of important application areas is standard-definition (SD) video processing to get high-definition (HD) content for television and broadcast. However, high-quality up-conversion is a challenging task. Most practical implementations use spatial domain processing such as video frame interpolation for video up-scale. Meanwhile, due to sampling limitation the high-frequency component of output HD video cannot be efficiently reconstructed by applying only the spatial domain processing and high-quality up-conversion usually requires temporal domain processing as well. The authors propose practical implementation of such up-conversion technique providing significantly better visual results in comparison to traditional methods of SD to HD up-conversion.
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References
Borman, S., Stevenson, R.: Super-resolution from image sequences - a review. In: Proc. Midwest Symposium on Circuits and Systems (1998)
Kim, K.I., Franz, M.O., Scheolkopf, B.: Kernel Hebbian Algorithm for Single-Frame Super-Resolution. In: Proc. Midwest Symposium on Circuits and Systems (1998)
Borman, S., Stevenson, R.: Spatial Resolution Enhancement of Low-Resolution Image Sequences - A Comprehensive Review with Directions for Future Research, Department of Electrical Engineering, University of Notre Dame (1998)
Farsiu, S., Dirk Robinson, M., (Student Member), Elad, M., Milanfar, P., (Senior Member): Fast and Robust Multiframe Super Resolution (1998)
Yuan, S., Abe, M., Taguchi, A., Kawamata, M.: High accuracy wadi image interpolation with local gradient features. In: Proc. of 2005 Int. Symposium on Intelligent Signal Proc. and Comm. Systems, pp. 85–88 (2005)
Lukin, A., Kubasov, D.: High-Quality Algorithm for Bayer Pattern Interpolation. Programming and Computer Software 30(6), 347–358 (2004)
Li, X., Orchard, M.: New edge-directed interpolation. IEEE Trans. on Image Processing 10(10), 1521–1527 (2001)
Chughtai, M.A., Khattak, N.: An Edge Preserving Locally Adaptive Anti-aliasing Zooming Algorithm with Diffused Interpolation. In: The 3rd Canadian Conference on Computer and Robot Vision (CRV 2006), p. 49 (2006)
Rodrigues, L., Borges, D.L., Goncalves, L.M.: A Locally Adaptive Edge-Preserving Algorithm for Image Interpolation. In: Proceedings of the 15th Brazilian Symposium on Computer Graphics and Image Processing, pp. 300–305 (2002)
Duchon, C.E.: Lanczos Filtering in One and Two Dimensions. Journal of Applied Meteorology 18(8), 1016–1022 (1979)
Glassner, A.S., Turkowski, K., Gabriel, S.: Filters for Common Resampling Tasks. In: Graphics Gems I, pp. 147–165. Academic Press, London (1990)
Barreto, D., Alvarez, L.D., Abad, J.: Motion Estimation Techniques in Super-Resolution Image Reconstruction. In: A Performance Evaluation. Virtual observatory. Plate content digitalization, archive mining and image sequence processing, Sofia, Bulgary, vol. 1, pp. 254–268 (2006)
Richardson, E.G.: Iain: H.264 and MPEG-4 Video Compression: Video Coding for Next-generation Multimedia. John Wiley and Sons Ltd, Chichester (2003)
Brown, L.G.: Computing Surveys (CSUR). Columbia Univ., ACM, New York (December 1992)
Aptoula, E., Lefevre, S., Ronse, C.: A hit-or-miss transform for multivariate images Source Pattern Recognition Letters, pp. 760–764. Elsevier Science Inc., New York (June 2009)
Khosravi, M., Schafer, R.W.: Template matching based on a grayscale hit-or-miss transform. Human Interface Technol. Center, ATT Global Inf. Solutions, Atlanta, GA. IEEE Trans. Image Process. (1996)
Perret, B., Lefevre, S., Collet, C.: A robust hit-or-miss transform for template matching applied to very noisy astronomical images. Source, Pattern Recognition 42(11), 2470–2480 (2009)
Khosravi, M., Schafer, R.W.: Template Matching Based on a Grayscale Hit-or-Miss Transform. IEEE Transactions on Image Processing 5(6) (June 1996)
Tekalp, A.M., Ozkan, M.K., Sezan, M.I.: Highresolution image reconstruction from lower-resolution image sequences and space-varying image restoration. In: ICASSP, San Francisco, vol. III, pp. 169–172 (1992)
Chen, T.: Adaptive temporal interpolation using bidirectional motion estimation and compensation. In: IEEE International Conference of Image Processing, pp. 313–316 (2002)
Chan, T.-M., Zhang, J., Pu, J., Huang, H.: Neighbor embedding based super-resolution algorithm through edge detection and feature selection. Pattern Recognition Letters 30(5), 494–502 (2009)
Drettakis, G., Scopigno, R.: Visual-Quality Optimizing Super Resolution. Eurographics 27(3) (2008)
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Vashkelis, V., Trukhina, N., Kumar, S. (2010). Practical Implementation of Super-Resolution Approach for SD-to-HD Video Up-Conversion. In: Qiu, G., Lam, K.M., Kiya, H., Xue, XY., Kuo, CC.J., Lew, M.S. (eds) Advances in Multimedia Information Processing - PCM 2010. PCM 2010. Lecture Notes in Computer Science, vol 6297. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-15702-8_8
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DOI: https://doi.org/10.1007/978-3-642-15702-8_8
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