HMCS Vancouver Pre-Deployment Brief - May 2024 (Web Version).pptx
Project pptVLSI ARCHITECTURE FOR AN IMAGE COMPRESSION SYSTEM USING VECTOR QUANTIZATION
1. VLSI ARCHITECTURE FOR
AN IMAGE COMPRESSION
SYSTEM USING VECTOR
QUANTIZATION
Presented by:
DEBASISH PAIKARAY
PRATYUSH KU. SAHOO
SAUMYA RANJAN NANDA
ABINASH MISHRA
Guided By: Mr. P.K.NANDA
Asst. Professor
Dept. of ECE
3. Motivation
Better Result can be achieved by
Multistage Vector Quantization over
Single stage Vector Quantization.
4. Objective
To propose a VLSI Architecture for an image
compression system using Vector Quantization
5. Introduction
Data compression is a process of reducing the
amount of data required to represent a given
quantity of information, so that it takes lesser
storage space and lesser transmission time than
the data which is not compressed.
A fundamental goal of data compression is to
reduce the bit rate for transmission or data storage
while maintaining an acceptable fidelity or image
quality.
6. Fundamentals
• R = 1 – (1/C );
C = b / b’ C =compression ratio
•If C = 10 (or 10:1), for larger representation has
10 bits of data for every 1 bit of data in smaller
representation.
So, R = 0.9, indicating that 90 % of its data is
redundant.
8. Distortion Measures
The size of the error relative to the signal
is given by the signal-to-noise ratio (SNR)
Another common measure is the peak-
signal-to-noise ratio (PSNR)
The average pixel difference is given by the
Mean Square Error (MSE)
32. Application of vector quantization
Vector quantization technique is
efficiently used in various areas of
biometric modalities like finger print
pattern recognition ,face recognition by
generating codebooks of desired size.
33. Conclusion
We have successfully designed an efficient
codebook using LBG Algorithm &
proposed an cost effective MSVQ VLSI
architecture for an Image compression
system.
34. REFERENCES
1.Y. Linde, A. Buzo, and R. M. Gray, “An algorithm for vector quantizer design,” IEEE
Trans. Commun., vol. COM-28, pp. 84-95, Jan. 1980.
2.Nasser m. Nasrabadi, Membeire,E E E,A nd robert A. King,” Image Coding Using Vector
Quantization: A Review” IEEE Transactions on Communications, vol. 36, no. 8, august 1988
3. A. K. Jain, “Image data compression: A review,” Proc. IEEE, vol. 69, pp. 349-389, Mar.
1981.
4. A. Buzo, A. H. Gray, R. M. Gray, and J. D. Markel, “Speech coding based upon vector
quantization,” IEEE Trans. Acoust. Speech, Signal Processing, vol. ASSP-28, pp. 562-
574, Oct. 1980.
5. R. M. Gray, “Vector quantization,” IEEE ASSP Mag., pp. 4-29, Apr. 1984.
6.Khalid Sayood ,”Introduction to Image Compression”,3rd edition
7. Seung-Kwon Paek and Lee-Sup Kim,”A Real Time Wavelet VQ Algorithm and Its VLSI
Architecture”, IEEE Transaction on Circuits & Systems for video Technology, Vol. 10, No.
3,April 2000.
8. Tzu-Chuen Lu, Ching-Yun Chang, “A Survey of VQ Codebook Generation” , Journal of
Information Hiding and Multimedia Signal Processing, Volume 1, Number 3, July 2010.
9. Jyoti Singhai and Rakesh singhai,”MSVQ: A Data compression technique foe multimedia
application”,Journal of Scientific & Industrial Research, Vol. 65,December 2006,pp. 982-985.