Abstract
With the projected significant growth in mobile internet and multimedia services, there is a strong demand for next-generation wireless appliances capable of image communication. However, wireless image communication faces significant bottlenecks including high energy and bandwidth consumption. Past studies have shown that the bottlenecks to wireless image communication can be overcome by developing adaptive image compression algorithms and dynamically adapting them to current channel conditions and service requirements [1],[2].
In this paper, we present the design of an adaptive hardware/software architecture that enables adaptive wireless image communication. Through intelligent co-design of the proposed architecture and algorithms, we achieve an architecture which enables not only power and performance efficient implementation, but also fast and efficient run-time adaptation of image compression parameters. To achieve efficient image compression and run-time adaptation, we characterized the adaptation needs of an adaptive image compression algorithm in terms of parameters, and implemented an adaptive hardware/software architecture capable of executing JPEG image compression with different parameters. We present experimental results demonstrating that the proposed architecture enables low overhead adaptation to current wireless conditions and requirements while implementing a low cost (energy and performance) implementation of adaptive image compression algorithms.
This work is supported by the UCSD Center for Wireless Communications, UC CoRe, and SRC
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
C. N. Taylor, S. Dey, and D. Panigrahi, “Energy/Latency/Image Quality Trade-offs in Enabling Mobile Multimedia Communication”, in Software Radio—Technologies and Services (E. D. Re, ed.), pp. 55–66, Springer Verlag, 2001.
C. N. Taylor and S. Dey, “Adaptive Image Compression for Enabling Mobile Multimedia Communication”, in In Proceedings of IEEE International Conference on Communications, 2001.
S. Kallel, S. Bakhtiyari, and R. Link, “An Adaptive Hybrid ARQ Scheme”, Wireless Personal Communications, vol. 12, pp. 297–311, March 2000.
P. Cherriman and L. Hanzo, “Error-rate Based Power-controlled Multimode H.263-Assisted Video Telephony”, IEEE Transactions on Vehicular Technology, vol. 48, pp. 1726–38, September 1999.
M. Goel, S. Appadwedula, N. R. Shanbhag, K. Ramchandran, and D. L. Jones, “A Low-power Multimedia Communication System for Indoor Wireless Applications”, in 1999 IEEE Workshop on Signal Processing Systems. SiPS 99, pp. 473–82, October 1999.
P. F. Joseph A. Fisher and G. Desoli, “Custom-Fit Processors: Letting Applications Define Architectures”, in Proceedings of the 29th IEEE/ACM International Symposium on Microarchitecture, 1996.
C. Ebeling, D. C. Cronquist, and P. Franklin, “RaPiD-Recon.gurable Pipelined Datapath”, in The 6th International Workshop on Field-Programmable Logic and Applications, 1996.
J. R. Hause and J. Wawrzynek, “Garp: A MIPS Processor with a Reconfigurable Coprocessor”, in The 5th Annual IEEE Symposium on Field-Programmable Custom Computing Machines, April 1997.
R. D. Wittig and P. Chow, “OneChip: An FPGA Processor With Reconfigurable Logic”, in IEEE Symposium on FPGAs for Custom Computing Machines, April 1996.
Y. Li, T. Callahan, E. Darnell, R. Harr, U. Kurkure, and J. Stockwood, “Hardware-Software Co-Design of Embedded Reconfigurable Architectures”, in Proceedings, 37th Design Automation Conference, June 2000.
G. K. Wallace, “The JPEG still picture compression standard”, in IEEE Transactions on Consumer Electronics, vol. 38, February 1992.
J. Bracamonte, M. Ansorge, and F. Pellandini, “VLSI systems for image compression. A power-consumption/image-resolution trade-off approach”, in Proceedings of the SPIE-The International Society for Optical Engineering, vol. 2952, pp. 591–6, October 1996.
A. Said and W. A. Pearlman, “A New, Fast, and Efficient Image Codec Based on Set Partitioning in Hierarchical Trees”, IEEE Transactions on Circuits and Systems for Video Technology, vol. 6, June 1996.
M. A. Lepley and R. D. Forkert, “AWIC: Adaptive Wavelet Image Compression”, tech. rep., MITRE, September 1997.
C. Christopoulos, A. Skodras, and T. Ebrahimi, “The JPEG2000 Still Image Coding System: An Overview”, IEEE Transactions on Consumer Electronics, vol. 46, pp. 1103–1127, November 2000.
“PicoJava MicroProcessor Core,” Sun Microsystems, http://www.sun.com/microelectronics/picoJava.
D. Panigrahi, C. N. Taylor, and S. Dey, “Interface Based Hardware/Software Validation of a System-on-Chip”, in Proceedings of 5th IEEE HLDVT Workshop, November 2000.
UMC Group, 0.18um 1P6M Logic Process Interconnect Capacitance Model (Rev. 1.2), July 1999.
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2002 Springer-Verlag Berlin Heidelberg
About this chapter
Cite this chapter
Taylor, C.N., Panigrahi, D., Dey, S. (2002). Design of an Adaptive Architecture for Energy Efficient Wireless Image Communication. In: Deprettere, E.F., Teich, J., Vassiliadis, S. (eds) Embedded Processor Design Challenges. SAMOS 2001. Lecture Notes in Computer Science, vol 2268. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45874-3_15
Download citation
DOI: https://doi.org/10.1007/3-540-45874-3_15
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-43322-4
Online ISBN: 978-3-540-45874-6
eBook Packages: Springer Book Archive