Simulation Based VLSI Implementation of Fast Efficient Lossless Image Compression System Using Adjusted Binary Code & Golumb Rice Code

The Simulation based VLSI Implementation of
FELICS (Fast Efficient Lossless Image Compression System)
Algorithm is proposed to provide the lossless image compression and
is implemented in simulation oriented VLSI (Very Large Scale
Integrated). To analysis the performance of Lossless image
compression and to reduce the image without losing image quality
and then implemented in VLSI based FELICS algorithm. In FELICS
algorithm, which consists of simplified adjusted binary code for
Image compression and these compression image is converted in
pixel and then implemented in VLSI domain. This parameter is used
to achieve high processing speed and minimize the area and power.
The simplified adjusted binary code reduces the number of arithmetic
operation and achieved high processing speed. The color difference
preprocessing is also proposed to improve coding efficiency with
simple arithmetic operation. Although VLSI based FELICS
Algorithm provides effective solution for hardware architecture
design for regular pipelining data flow parallelism with four stages.
With two level parallelisms, consecutive pixels can be classified into
even and odd samples and the individual hardware engine is
dedicated for each one. This method can be further enhanced by
multilevel parallelisms.





References:
[1] Chenwei Deng, Weisi Lin “Content- based Image Compression for
Arbitrary-Resolution Display Devices” IEEE transactions on
multimedia, vol.14, no. 4, August 2012.
[2] Tsung-Han Tsai,Yu-Hsuan Lee and Yu-Yu Lee,“Design and Analysis of
High-Throughput Lossless Image Compression Engine Using VLSIOriented
FELICS Algorithm” in IEEE Transactions On Very Large
Scale Integration (VLSI) Systems, Vol. 18, NO. 1, pp.39-52,Jan 2010.
[3] L. Xiaowen, X. Chen, X. Xie, G. Li, L. Zhang, C. Zhang, and Z.Wang,
“A low power, fully pipelined JPEG-LS encoder for lossless image
compression,” in Proc. IEEE Int. Conf. Multimedia EXPO, pp. 1906–
1909, 2007. [4] W. D. Len-Salas, S. Balkir, K. Sayood, N. Schemm, and M. W.
Hoffman, “A CMOS imager with focal plane compression using
predictive coding,” IEEE J. Solid-State Circuits, vol. 42, no. 11, pp.
2555–2572, Nov. 2007.
[5] L. Yang, H. Lekatsas, and R. P. Dick, “High-performance operating
system controlled memory compression,” in Proc. Int. Conf. Des.
Autom. Conf., Jul. 2006.
[6] R. Mehboob, S. A. Khan, and Z. Ahmed, “High speed lossless data
compression architecture,” in Proc. IEEE Int. Conf. Multitopic, pp. 84–
88, 2006.
[7] X. Xie, G. L. Li, X. K. Chen, C. Zhang, and Z. H. Wang, “A low
complexity near-lossless image compression method and its ASIC
design for wireless endoscopy system,” in Proc. Int. Conf. ASICON,
2005.
[8] C.-C. Cheng, P.-C. Tseng, C.-T. Huang, and L.-G. Chen, “Multi-mode
embedded compression codec engine for power-aware video coding
system,” in Proc. IEEE Workshop. Signal Process. Syst., 2005.
[9] P. Corsonello, S. Perri, P. Zicari, and G. Cocorullob, "Microprocessor
based FPGA implementation of SPIHT image compression subsystems,”
Microprocess. Microsyst., vol. 29, no. 6, pp. 299–305, Aug.2005.
[10] M. Milward, J. L. Nunez, and D. Mulvaney, “Design and
implementation of a lossless parallel high-speed data compression
system,” IEEE Trans. Parallel Distrib. Syst., vol. 15, no. 6, pp. 481–490,
Jun. 2004.