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

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Project pptVLSI ARCHITECTURE FOR AN IMAGE COMPRESSION SYSTEM USING VECTOR QUANTIZATION Project pptVLSI ARCHITECTURE FOR AN IMAGE COMPRESSION SYSTEM USING VECTOR QUANTIZATION Presentation Transcript

  • 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
  • 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
  • Motivation Better 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 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)
  • 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 encoding 1-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.
  • 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.
  • THANK YOU ALL…..