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Analysis on CFA Image Compression Methods

EE398A Final Project. Analysis on CFA Image Compression Methods. Sung Hee Park (shpark7@stanford.edu) Albert No (albertno@stanford.edu). EE398A Final Project. Outline. What is CFA? Motivation CFA Compression Methods Simulation Results and Images Conclusion.

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Analysis on CFA Image Compression Methods

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  1. EE398A Final Project Analysis on CFA Image Compression Methods Sung Hee Park (shpark7@stanford.edu) Albert No (albertno@stanford.edu)

  2. EE398A Final Project Outline What is CFA? Motivation CFA Compression Methods Simulation Results and Images Conclusion

  3. EE398A Final Project What is CFA? Color Filter Array (CFA) True RGB Image CFA Image Demosaic

  4. EE398A Final Project Motivation Current Image Compression Image Capturing System Image Display System CFA Image Demosaic Compression Decompression Transmission or Storage

  5. EE398A Final Project Motivation CFA Image Compression Image Capturing System Image Display System CFA Image Demosaic Compression Decompression Transmission or Storage

  6. EE398A Final Project Motivation Benefits for CFA Image Compression ⊙ CFA image compressor begins with 1/3 of data compared to conventional RGB image compressor. →Efficient image compression ⊙ Computationally expensive demosaic process can be done on the decoder side which usually has much more computational power and resources. → Computationally efficient, power efficient on encoder side

  7. EE398A Final Project CFA Compression methods Filtering and Conversion Naive Method Direct JPEG Low-pass Filter 1 Low-pass Filter 2 Rearranging Structure Simple Merging Ideal Entropy Estimation Structure Conversion Single Pixel Structure Separation Joint 2x2 Bayer Simple Prediction Adaptive Prediction

  8. EE398A Final Project CFA Compression methods Naive Method Direct JPEG JPEG Compression

  9. EE398A Final Project CFA Compression methods Rearranging Structure Rearranging Or Filtering JPEG Compression JPEG Compression JPEG Compression

  10. EE398A Final Project CFA Compression methods Rearranging Structure Simple Merging Structure Conversion Structure Separation

  11. EE398A Final Project CFA Compression methods Filtering and Conversion Low-pass Filter 1 Low-pass Filter 2

  12. EE398A Final Project CFA Compression methods Ideal Entropy Estimation Single Pixel ⊙ Estimate pmf of individual pixels. Joint 2x2 Bayer • ⊙ Convert to YCbCr color space. • ⊙ Evaluate joint entropy of four pixels in a Bayer pattern. 12

  13. EE398A Final Project CFA Compression methods Ideal Entropy Estimation Simple Prediction ⊙ Use JPEG-LS nonlinear predictor to estimate 2x2 Bayer pattern 13

  14. EE398A Final Project CFA Compression methods Ideal Entropy Estimation Adaptive Prediction • ⊙ Context matching based prediction. • ⊙ Evaluate weighted sum of four candidate pixel values. • ⊙ Give higher weight to the one with similar support regions. 14

  15. EE398A Final Project Simulation Results and Images ⊙ One small test! ⊙ Use JPEG to compress the above images. (Quality 100) ⊙ RGB: 271.9 kByte, CFA: 314.4 kByte ⊙ Chroma subsampling ⊙ High frequency contents in CFA 15

  16. EE398A Final Project Simulation Results and Images Direct JPEG Rate = 1.0112 bpp PSNR = 25.9613 dB Rate = 2.0882 bpp PSNR = 34.3955 dB Demosaic method : bilinear 16

  17. EE398A Final Project Simulation Results and Images Structure Conversion Rate = 0.2699 bpp PSNR = 29.7773 dB Rate = 0.5799 bpp PSNR = 32.8548 dB Demosaic method : frequency 17

  18. EE398A Final Project Simulation Results for Lossless Cases All 24 Kodak images 18

  19. EE398A Final Project Reconstructed CFA Image Quality Compression Decompression Error Component 19

  20. EE398A Final Project Simulation Results for Lossy Compression First 12 Kodak images 20

  21. EE398A Final Project Reconstructed RGB Image Quality Demosaic Error Component Compression Decompression Demosaic 21

  22. EE398A Final Project Simulation Results for Lossy Compression First 12 Kodak images 22

  23. EE398A Final Project Simulation Results for Lossy Compression First 12 Kodak images 23

  24. EE398A Final Project Simulation Results for Lossy Compression ⊙ Bilinear interpolation introduces very high frequency chrominance components which are easily corrupted by compression process. Adaptive frequency domain, 3.4588 bpp, 38.7074 dB Bilinear, 3.9113 bpp, 32.2482 dB 24

  25. EE398A Final Project Simulation Results for Lossy Compression First 12 Kodak images 25

  26. EE398A Final Project Simulation Results for Lossy Compression ⊙ Sophisticated demosaick algorithms try to preserve edge information which are wrongly introduced by compression artifact. Adaptive homogeneity, 0.3390 bpp, 29.9042 dB Bilinear, 0.3390 bpp, 32.1840 dB 26

  27. EE398A Final Project Conclusion ⊙ CFA image compression can be effectively done by using existing JPEG encoding pipeline. ⊙ Overall performance of CFA image compression is affected by the choice of demosaicking algorithm. ⊙ If bilinear interpolation is used, CFA image compression is better than the conventional 3-ch image compression. ⊙ If more complicated demosaic operation is used, it is possible that CFA image compression is the worse choice. 27

  28. EE398A Final Project Thank you Questions?

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