Project pptVLSI ARCHITECTURE FOR AN IMAGE COMPRESSION SYSTEM USING VECTOR QUANTIZATION

VLSI ARCHITECTURE FOR AN IMAGE COMPRESSION SYSTEM USING VECTOR QUANTIZATION

Presented by:DEBASISH PAIKARAYPRATYUSH KU. SAHOOSAUMYA RANJAN NANDAABINASH MISHRA

Guided By: Mr. P.K.NANDA Asst. Professor Dept. of ECE

TALK FLOW Motivation Objective Introduction Image compression techniques Distortion measures Scalar quantization Vector quantization LBG Algorithm MSVQ VLSI Architecture of MSVQ Cost effective VLSI Architecture of MSVQ Results & Analysis Conclusion Reference

MotivationBetter Result can be achieved by Multistage Vector Quantization over Single stage Vector Quantization.

Objective

To propose a VLSI Architecture for an image

compression system using Vector Quantization

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.

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.

Compression Techniques

2 types of compression techniques:

1) Lossless Compression: Examples : Scalar Quantization

2) Lossy Compression: Examples : JPEG , VQ

Distortion MeasuresThe 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)

Scalar quantization

y=Q(x)

y =Q(x) Q: R C

Where R is the real line C={y1, y2,…, yN}

Vector quantization

A generalization of scalar quantization to quantization of a vector

Scalar quantization Vector quantization

Vector Quantization encoding1-D ANALYSIS:

2-D ANALYSIS:

Important Terminologies

1.Euclidean Space

2.Vornoi Region

3.Code Vector(Each red dot)

4.Code Word(16 red dot)

5.Index

Diagramatic Representation of compression & decompression using VQ

VQ procedure

LBG Algorithm

Training Vector=X1(7,10,14,6)

Finding out the perfect codebook:

Distance Calculation:

Proposed Image Coding Scheme

MSVQ(Multistage VQ)

Block diagram of three stage Multistage Vector Quantizer

Different subbands of Image

VLSI architecture for MSVQ

Cost-effective VLSI architecture for MSVQ

VLSI architecture of MDC(IPU & DCU)

High-performance MDC VLSI architecture

Result of LBG Algorithm

Decompressed Image

Original Image Compressed Image

TBW Diagram of Multiplier

TBW Diagram of Buffer

TBW Diagram of Adder

TBW diagram of MUX

Write mode operation of RAM

Read mode operation of RAM

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.

Conclusion

We have successfully designed an efficient codebook using LBG Algorithm & proposed an cost effective MSVQ VLSI architecture for an Image compression system.

REFERENCES1.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.

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