A VLSI architecture of JPEG2000 encoder

Copyright @ 2004 IEEEThis paper proposes a VLSI architecture of JPEG2000 encoder, which functionally consists of two parts: discrete wavelet transform (DWT) and embedded block coding with optimized truncation (EBCOT). For DWT, a spatial combinative lifting algorithm (SCLA)-based scheme with both 5/3 reversible and 9/7 irreversible filters is adopted to reduce 50% and 42% multiplication computations, respectively, compared with the conventional lifting-based implementation (LBI). For EBCOT, a dynamic memory control (DMC) strategy of Tier-1 encoding is adopted to reduce 60% scale of the on-chip wavelet coefficient storage and a subband parallel-processing method is employed to speed up the EBCOT context formation (CF) process; an architecture of Tier-2 encoding is presented to reduce the scale of on-chip bitstream buffering from full-tile size down to three-code-block size and considerably eliminate the iterations of the rate-distortion (RD) truncation.This work was supported in part by the China National High Technologies Research Program (863) under Grant 2002AA1Z142

Layer Selection in Progressive Transmission of Motion-Compensated JPEG2000 Video

MCJ2K (Motion-Compensated JPEG2000) is a video codec based on MCTF (Motion- Compensated Temporal Filtering) and J2K (JPEG2000). MCTF analyzes a sequence of images, generating a collection of temporal sub-bands, which are compressed with J2K. The R/D (Rate-Distortion) performance in MCJ2K is better than the MJ2K (Motion JPEG2000) extension, especially if there is a high level of temporal redundancy. MCJ2K codestreams can be served by standard JPIP (J2K Interactive Protocol) servers, thanks to the use of only J2K standard file formats. In bandwidth-constrained scenarios, an important issue in MCJ2K is determining the amount of data of each temporal sub-band that must be transmitted to maximize the quality of the reconstructions at the client side. To solve this problem, we have proposed two rate-allocation algorithms which provide reconstructions that are progressive in quality. The first, OSLA (Optimized Sub-band Layers Allocation), determines the best progression of quality layers, but is computationally expensive. The second, ESLA (Estimated-Slope sub-band Layers Allocation), is sub-optimal in most cases, but much faster and more convenient for real-time streaming scenarios. An experimental comparison shows that even when a straightforward motion compensation scheme is used, the R/D performance of MCJ2K competitive is compared not only to MJ2K, but also with respect to other standard scalable video codecs

Scalable video/image transmission using rate compatible PUM turbo codes

The robust delivery of video over emerging wireless networks poses many challenges due to the heterogeneity of access networks, the variations in streaming devices, and the expected variations in network conditions caused by interference and coexistence. The proposed approach exploits the joint optimization of a wavelet-based scalable video/image coding framework and a forward error correction method based on PUM turbo codes. The scheme minimizes the reconstructed image/video distortion at the decoder subject to a constraint on the overall transmission bitrate budget. The minimization is achieved by exploiting the rate optimization technique and the statistics of the transmission channel

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

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