Project pptVLSI ARCHITECTURE FOR AN IMAGE COMPRESSION SYSTEM USING VECTOR QUANTIZATION

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Project pptVLSI ARCHITECTURE FOR AN IMAGE COMPRESSION SYSTEM USING VECTOR QUANTIZATION

  1. 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
  2. 2. 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
  3. 3. Motivation Better Result can be achieved by Multistage Vector Quantization over Single stage Vector Quantization.
  4. 4. Objective To propose a VLSI Architecture for an image compression system using Vector Quantization
  5. 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. 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.
  7. 7. Compression Techniques 2 types of compression techniques: 1) Lossless Compression: Examples : Scalar Quantization 2) Lossy Compression: Examples : JPEG , VQ
  8. 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)
  9. 9. Scalar quantization y=Q(x) y =Q(x) Q: R  C Where R is the real line C={y1, y2,…, yN}
  10. 10. Vector quantization A generalization of scalar quantization to quantization of a vector Scalar quantization Vector quantization
  11. 11. Vector Quantization encoding 1-D ANALYSIS:
  12. 12. 2-D ANALYSIS:
  13. 13. Important Terminologies 1.Euclidean Space 2.Vornoi Region 3.Code Vector(Each red dot) 4.Code Word(16 red dot) 5.Index
  14. 14. Diagramatic Representation of compression & decompression using VQ
  15. 15. VQ procedure
  16. 16. LBG Algorithm Training Vector=X1(7,10,14,6)
  17. 17. Finding out the perfect codebook: Distance Calculation:
  18. 18. Proposed Image Coding Scheme
  19. 19. MSVQ(Multistage VQ)
  20. 20. Block diagram of three stage Multistage Vector Quantizer
  21. 21. Different subbands of Image
  22. 22. VLSI architecture for MSVQ
  23. 23. Cost-effective VLSI architecture for MSVQ
  24. 24. VLSI architecture of MDC(IPU & DCU)
  25. 25. High-performance MDC VLSI architecture
  26. 26. Result of LBG Algorithm Decompressed Image Original Image Compressed Image
  27. 27. TBW Diagram of Multiplier
  28. 28. TBW Diagram of Buffer TBW Diagram of Adder TBW diagram of MUX
  29. 29. Write mode operation of RAM Read mode operation of RAM
  30. 30. 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.
  31. 31. Conclusion We have successfully designed an efficient codebook using LBG Algorithm & proposed an cost effective MSVQ VLSI architecture for an Image compression system.
  32. 32. 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.
  33. 33. THANK YOU ALL…..

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