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Distributed Image Coding with Improved Estimation of Disparity and Noise

Distributed Image Coding with Improved Estimation of Disparity and Noise. David Chen Stanford University EE398B Final Project. Outline. Existing distributed image coder Interpolation of disparity field to pixel resolution Edge-based noise estimation Shape-based disparity estimation

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Distributed Image Coding with Improved Estimation of Disparity and Noise

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  1. Distributed Image Coding with Improved Estimation of Disparity and Noise David Chen Stanford University EE398B Final Project

  2. Outline • Existing distributed image coder • Interpolation of disparity field to pixel resolution • Edge-based noise estimation • Shape-based disparity estimation • Experimental results • Bit savings for lossless coding • PSNR improvements for lossy coding • Faster convergence of decoding algorithm • Removal of blocking artifacts • Suggested future work David Chen, Stanford University, EE398B Final Project

  3. Existing distributed image coder [D. Varodayan et. al, 2007] David Chen, Stanford University, EE398B Final Project

  4. Existing distributed image coder • Disparity block size k mediates tradeoff between resolution and noise suppression • Nearest neighbor interpolation to pixel resolution Dblock(u,v) D(x,y) David Chen, Stanford University, EE398B Final Project

  5. Existing distributed image coder • Noise modeled as stationary, zero-mean, Laplacian random field David Chen, Stanford University, EE398B Final Project

  6. Existing distributed image coder • Initial disparity estimate can significantly influence final solution at low rates David Chen, Stanford University, EE398B Final Project

  7. New distributed image coder David Chen, Stanford University, EE398B Final Project

  8. Bilinear interpolation of disparity field • Increases disparity field to pixel resolution • Achieves smooth transitions between block centers Dblock(u,v) D(x,y) David Chen, Stanford University, EE398B Final Project

  9. Bilinear interpolation of disparity field • Yields 3-5% bit savings in lossless coding over nearest neighbor interpolation 1 = nearest neighbor interpolation 2 = bilinear interpolation 3 = conditional entropy Test data: 72-by-88, 8-bpp bear images David Chen, Stanford University, EE398B Final Project

  10. Edge-based noise estimation • Extraction using Canny edge detector [J. Canny, 1986] error image N edge image Yedge David Chen, Stanford University, EE398B Final Project

  11. Edge-based noise estimation • Yields additional 3% bit savings in lossless coding 1 = nearest neighbor 2 = bilinear 3 = bilinear + new noise model 4 = conditional entropy Test data: 72-by-88, 8 bpp bear images David Chen, Stanford University, EE398B Final Project

  12. Shape-based disparity estimation • In early iterations, replace noisy disparity estimate with shape-based disparity estimate • In later iterations, switch back to block-based estimator followed by bilinear interpolation ideal field segmented image noisy field David Chen, Stanford University, EE398B Final Project

  13. Shape-based disparity estimation • Shape segmentation using graph partitioning algorithm [P. F. Felzenszwalb and D. P. Huttenlocher, 2004] David Chen, Stanford University, EE398B Final Project

  14. Shape-based disparity estimation • Yields additional 3% bit savings in lossless coding for one test image • Yields 4 dB PSNR gain in lossy coding at low rates (- +) Previous system (- o) New system Test data: 72-by-88, 8 bpp bear images David Chen, Stanford University, EE398B Final Project

  15. Faster convergence in decoding • New decoder can converge in fewer iterations of LDPC belief propagation (- +) Previous system (- o) New system Test data: 72-by-88, 8 bpp bear images David Chen, Stanford University, EE398B Final Project

  16. Removal of blocking artifacts • Bilinear interpolation of disparity and shape-based disparity initialization eliminate blocking artifacts previous decoder proposed decoder Test data: 72-by-88, 8 bpp bear images, rate = 3.87 bpp David Chen, Stanford University, EE398B Final Project

  17. Summary of project results • Improved distributed LPDC image coder • Bilinear interpolation of block disparity field • Nonstationary edge-based noise model • Shape-based disparity initialization • Gains for both lossless and lossy coding • 5-8% bit savings for lossless coding • 4 dB PSNR increase for lossy coding • Faster convergence in LDPC belief propagation • Removal of blocking artifacts • Possible future work • Unequal protection of source bits to reduce edge errors David Chen, Stanford University, EE398B Final Project

  18. References • D. Slepian and J. K. Wolf, “Noiseless coding of correlated information sources,” IEEE Transactions on Information Theory, vol. 19, no. 4, pp. 471-480, July 1973. • D. Varodayan, A. Mavlankar, M. Flierl, and B. Girod, “Distributed grayscale stereo image coding with unsupervised learning of disparity,” in Proceedings of IEEE Data Compression Conference, pp. 143-152, March 2007. • J. Canny, “A computational approach to edge detection,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 8, no. 6, pp. 679-714, Nov. 1986. • P. F. Felzenszwalb and D. P. Huttenlocher, “Efficient graph-based image segmentation,” International Journal of Computer Vision, vol. 59, no. 2, Sept. 2004. David Chen, Stanford University, EE398B Final Project

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