Rate-control algorithms for non-embedded wavelet-based image coding

Abstract

During the last decade, there has been an increasing interest in the design of very fast wavelet image encoders focused on specific applications like interactive real-time image and video systems, running on power-constrained devices such as digital cameras, mobile phones where coding delay and/or available computing resources (working memory and power processing) are critical for proper operation. In order to reduce complexity, most of these fast wavelet image encoders are non-(SNR)-embedded and as a consequence, precise rate control is not supported. In this work, we propose some simple rate control algorithms for these kind of encoders and we analyze their impact to determine if, despite their inclusion, the global encoder is still competitive with respect to popular embedded encoders like SPIHT and JPEG2000. In this study we focus on the non-embedded LTW encoder, showing that the increase in complexity due to the rate control algorithm inclusion, maintains LTW competitive with respect to SPIHT and JPEG2000 in terms of R/D performance, coding delay and memory consumption. © Springer Science+Business Media, LLC 2011This work was funded by Spanish Ministry of education and Science under grant DPI2007-66796-C03-03.Lopez Granado, OM.; Onofre Martinez-Rach, M.; Pinol Peral, P.; Oliver Gil, JS.; Perez Malumbres, MJ. (2012). Rate-control algorithms for non-embedded wavelet-based image coding. Journal of Signal Processing Systems. 68(2):203-216. https://doi.org/10.1007/s11265-011-0598-6S203216682Antonini, M., Barlaud, M., Mathieu, P., & Daubechies, I. (1992). Image coding using wavelet transform. IEEE Transaction on Image Processing, 1(2), 205–220.Cho, Y., & Pearlman, W.A. (2007). Hierarchical dynamic range coding of wavelet subbands for fast and efficient image compression. IEEE Transactions on Image Processing, 16, 2005–2015.Chrysafis, C., Said, A., Drukarev, A., Islam, A., & Pearlman, W. (2000). SBHP—A low complexity wavelet coder. In IEEE international conference on acoustics, speech and signal processing.CIPR: http://www.cipr.rpi.edu/resource/stills/kodak.html . Center for Image Processing Research.Davis, P. J. (1975) Interpolation and approximation. Dover Publications.Grottke, S., Richter, T., & Seiler, R. (2006). Apriori rate allocation in wavelet-based image compression. In Second international conference on automated production of cross media content for multi-channel distribution, 2006. AXMEDIS ’06 (pp. 329–336). doi: 10.1109/AXMEDIS.2006.12 .Guo, J., Mitra, S., Nutter, B., & Karp, T. (2006). Backward coding of wavelet trees with fine-grained bitrate control. Journal of Computers, 1(4), 1–7. doi: 10.4304/jcp.1.4.1-7 .ISO/IEC 10918-1/ITU-T Recommendation T.81 (1992). Digital compression and coding of continuous-tone still image.ISO/IEC 15444-1 (2000). JPEG2000 image coding system.Kakadu, S. (2006). http://www.kakadusoftware.com .Kasner, J., Marcellin, M., & Hunt, B. (1999). Universal trellis coded quantization. IEEE Transactions on Image Processing, 8(12), 1677–1687. doi: 10.1109/83.806615 .Lancaster, P. (1986). Curve and surface fitting: An introduction. Academic Press.Oliver, J., & Malumbres, M. (2001). A new fast lower-tree wavelet image encoder. In Proceedings of international conference on image processing, 2001 (Vol. 3, pp. 780–783). doi: 10.1109/ICIP.2001.958236 .Oliver, J., & Malumbres, M. P. (2006). Low-complexity multiresolution image compression using wavelet lower trees. IEEE Transactions on Circuits and Systems for Video Technology, 16(11), 1437–1444.Pearlman, W. A. (2001). Trends of tree-based, set partitioning compression techniques in still and moving image systems. In Picture coding symposium.Said, A., & Pearlman, A. (1996). A new, fast and efficient image codec based on set partitioning in hierarchical trees. IEEE Transactions on Circuits, Systems and Video Technology, 6(3), 243–250.Table Curve 3D 3.0 (1998). http://www.systat.com. Systat Software Inc.Wu, X. (2001). The transform and data compression handbook, chap. Compression of wavelet transform coefficients, (pp. 347–378). CRC Press.Zhidkov, N., & Kobelkov, G. (1987). Numerical methods. Moscow: Nauka