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VQ Encoded Image

VQ Encoded Image. Ex.3 Kahlil Muchtar D023010006. VQ Encoded Image. Original. VQ Image PSNR=30.1887. Proposed Method-Block Distance. Algorithm. Calculate the mean for every block (the size is the same as index size)

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VQ Encoded Image

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  1. VQ Encoded Image Ex.3 Kahlil Muchtar D023010006

  2. VQ Encoded Image Original VQ Image PSNR=30.1887

  3. Proposed Method-Block Distance

  4. Algorithm • Calculate the mean for every block (the size is the same as index size) • Calculate the distance between block and its 4 adjacent (N4 neighbours') • If the distance > threshold, it means, the block is regarded as random noise. • If the block is random noise, the index of that block replaced by one of its neighbours index. • Otherwise, corresponding index will be obtained from look up table.

  5. Illustrations Replaced by neighbours index x,y Calculate the Euclidean distance between block and its 4 neighbours. If distance > TH then, …

  6. 5% VQ PSNR = 23.9067 5% VQ Improved PSNR Improved = 25.5726

  7. 10% VQ PSNR = 20.1393 10% VQ Improved PSNR Improved = 22.4339

  8. Conclusions • Block distance can reduce the random noise (5%) but would be less accurate if noise is 10%. • Further improvement, instead of calculating the distance of entire neighbours, the clique concept may increase the accuracy.

  9. Appendix: Noise • intrnd = 0; • for(int y=0;y<height/BlockSize;y++){ • for(int x=0;x<width/BlockSize;x++){ • rnd = rand()%100; • tempx = rnd%(width/BlockSize); • tempy = rnd%(height/BlockSize); • if(rnd<10) // 10% • ClassY[y][x]=ClassY[tempy*BlockSize+y][tempx*BlockSize+x]; • } • }

  10. Appendix: Euclidean Distance • sumdistance = distance2+distance4+distance5+distance7; • if (sumdistance > 250) • { VQYI[y*BlockSize+j][x*BlockSize+i] = VQ[ClassY[y-1][x]][j][i]; • } • else • { • VQYI[y*BlockSize+j][x*BlockSize+i] = VQ[ClassY[y][x]][j][i]; • }

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