Liao huilian szu ti dsps lab aug 27 2007
This presentation is the property of its rightful owner.
Sponsored Links
1 / 35

Liao Huilian SZU TI-DSPs LAB Aug 27, 2007 PowerPoint PPT Presentation


  • 89 Views
  • Uploaded on
  • Presentation posted in: General

Optimizer based on particle swarm optimization and LBG (PSO-LBG) — application in vector quantization. Liao Huilian SZU TI-DSPs LAB Aug 27, 2007. School of Software Engineering, Shenzhen University. Outline. Vector quantization (VQ) LBG Particle swarm optimization (PSO)

Download Presentation

Liao Huilian SZU TI-DSPs LAB Aug 27, 2007

An Image/Link below is provided (as is) to download presentation

Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author.While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server.


- - - - - - - - - - - - - - - - - - - - - - - - - - E N D - - - - - - - - - - - - - - - - - - - - - - - - - -

Presentation Transcript


Liao huilian szu ti dsps lab aug 27 2007

Optimizer based on particle swarm optimization and LBG (PSO-LBG)

— application in vector quantization

Liao Huilian

SZU TI-DSPs LAB

Aug 27, 2007

School of Software Engineering, Shenzhen University


Outline

Outline

  • Vector quantization (VQ)

    • LBG

  • Particle swarm optimization (PSO)

  • Optimizer based on PSO and LBG (PSO-LBG)

    • PSO-LBG

    • 2-dimensional simulation

    • Performance comparison

  • Conclusion

  • Acknowledgement

School of Software Engineering, Shenzhen University


Outline1

Outline

  • Vector quantization (VQ)

    • LBG

  • Particle swarm optimization (PSO)

  • Optimizer based on PSO and LBG (PSO-LBG)

    • PSO-LBG

    • 2-dimensional simulation

    • Performance comparison

  • Conclusion

  • Acknowledgement

School of Software Engineering, Shenzhen University


Vector quantization vq

Vector quantization (VQ)

School of Software Engineering, Shenzhen University


Liao huilian szu ti dsps lab aug 27 2007

LBG

  • LBG, a well-known method of VQ, was proposed by Linde, Buzo and Gray in 1980

  • Apply two optimality criteria iteratively:

    • Nearest neighbour criterion during assigning training vectors

    • Centroid criterion during updating codewords (code vectors)

  • Drawbacks:

    • Local optimization

    • Sensitive to the selection of initial codebook

School of Software Engineering, Shenzhen University


Liao huilian szu ti dsps lab aug 27 2007

LBG

School of Software Engineering, Shenzhen University


Liao huilian szu ti dsps lab aug 27 2007

LBG

School of Software Engineering, Shenzhen University


Outline2

Outline

  • Vector quantization (VQ)

    • LBG

  • Particle swarm optimization (PSO)

  • Optimizer based on PSO and LBG (PSO-LBG)

    • PSO-LBG

    • 2-dimensional simulation

    • Performance comparison

  • Conclusion

  • Acknowledgement

School of Software Engineering, Shenzhen University


Particle swarm optimization

Particle swarm optimization

  • PSO was proposed by Eberhart and Kennedy in 1995

  • Advantages:

    • Simplicity of implementation

    • Few parameters

    • High convergence rate

  • Population based optimization

    • Remember the best location of itself (Pbest)

    • Remember the best experience in the swarm (Gbest)

School of Software Engineering, Shenzhen University


Particle swarm optimization1

Particle swarm optimization

School of Software Engineering, Shenzhen University


Outline3

Outline

  • Vector quantization (VQ)

    • LBG

  • Particle swarm optimization (PSO)

  • Optimizer based on PSO and LBG (PSO-LBG)

    • PSO-LBG

    • 2-dimensional simulation

    • Performance comparison

  • Conclusion

  • Acknowledgement

School of Software Engineering, Shenzhen University


Pso lbg

PSO-LBG

  • Based on conventional PSO and LBG algorithms

  • PSO-LBG

    • Structure of particle

    • Particle-pair model

    • Updating process

  • Apply in Vector Quantization (VQ)

School of Software Engineering, Shenzhen University


Structure of particle

Structure of particle

School of Software Engineering, Shenzhen University


Updating model

Updating model

School of Software Engineering, Shenzhen University


Updating process

Updating process

  • PSO-LBG performs three steps at each iteration:

    • Step1: Basic PSO operations

    • Step2: Classical vector quantizer, i.e. LBG algorithm

    • Step3: Deal with codewords “flying” over the boundary of training vector space

School of Software Engineering, Shenzhen University


Step1 basic pso operations

Step1-Basic PSO operations

  • Difference between PSO-LBG and PSO

    • Velocity updating: (additive inertia weight )

  • The parameters, and are much smaller than general PSO-based algorithms

  • Apply a particle-pair instead of a large number of particles

School of Software Engineering, Shenzhen University


Why small parameters

Why small parameters?

  • One point larger parameters

  • The solution of PSO-LBG represents N points in the training vector space

School of Software Engineering, Shenzhen University


Why just two particles

Why just two particles?

  • Three particles consisting of two codewords: P1={y1, y2}; P2={y2, y1} and P3={y3, y4}. P3 has a poorer performance

  • During the following iterations, particle P1andP2 are comparative

  • The fly direction of particle P3 is uncertain

School of Software Engineering, Shenzhen University


Why just two particles1

Why just two particles?

Stable convergence Unstable convergence

School of Software Engineering, Shenzhen University


Updating steps 2 3

Updating steps 2 & 3

  • Apply LBG with only 3 iterations to avoid converging early

  • Deal with the codewords “flying” over the boundary of search space: Replace this kind of codeword with the training vector that has higher distortion

School of Software Engineering, Shenzhen University


Demonstration in 2 dimensional space

Demonstration in 2-dimensional space

  • Three objectives PSO-LBG intends to achieve:

    • Disperse codewords

    • Move towards global optimum codebook

    • Codewords are settled reasonably both in high density regions and low density areas of training vectors space

School of Software Engineering, Shenzhen University


Demonstration in 2 dimensional space1

Demonstration in 2-dimensional space

LBG

=61.26

Initial codebook

=561.15

PSO-LBG

=46.85

School of Software Engineering, Shenzhen University


Performance comparison

Performance comparison

  • Performance is evaluated by and PSNR

    • : Mean square error between the training vectors and corresponding nearest codewords

    • PSNR: Peak signal to noise ratio

School of Software Engineering, Shenzhen University


Performance comparison1

Performance comparison

  • Comparison is conducted among:

    • LBG

    • Fuzzy k-means (FKM)

    • Fuzzy reinforced learning vector quantization (FRLVQ)

    • FRLVQ-FVQ: Apply FRLVQ as the pre-process of FVQ

    • PSO-LBG

School of Software Engineering, Shenzhen University


Experimental images

Experimental images

Pepper

Kgirl

Lena

Cameraman

School of Software Engineering, Shenzhen University


Psnr comparison on lena

PSNR comparison on Lena

School of Software Engineering, Shenzhen University


Convergence comparison on lena

Convergence comparison on Lena

School of Software Engineering, Shenzhen University


Computation time on lena

Computation time on Lena

School of Software Engineering, Shenzhen University


Codebook characteristic on lena

Codebook characteristic on Lena

School of Software Engineering, Shenzhen University


Psnr comparison on pepper

PSNR comparison on pepper

School of Software Engineering, Shenzhen University


Psnr comparison on cameraman

PSNR comparison on cameraman

School of Software Engineering, Shenzhen University


Psnr comparison on kgirl

PSNR comparison on Kgirl

School of Software Engineering, Shenzhen University


Conclusion

Conclusion

  • Experimental results demonstrate that PSO-LBG Outperforms existing algorithms in the field of vector quantization

  • Future work

    • Application in gene clustering

School of Software Engineering, Shenzhen University


Acknowledgement

Acknowledgement

  • My supervisor:

    Prof. Ji

  • All of you

School of Software Engineering, Shenzhen University


Thank you

Thank you!

School of Software Engineering, Shenzhen University


  • Login