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Block Loss Recovery Techniques for Image Communications

Block Loss Recovery Techniques for Image Communications. Jiho Park, D-C Park, Robert J. Marks, M. El-Sharkawi The Computational Intelligence Applications (CIA) Lab. Department of Electrical Engineering University of Washington May 29, 2002. Projections based Block Recovery – Motivation.

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Block Loss Recovery Techniques for Image Communications

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  1. Block Loss Recovery Techniques for Image Communications Jiho Park, D-C Park, Robert J. Marks, M. El-Sharkawi The Computational Intelligence Applications (CIA) Lab. Department of Electrical Engineering University of Washington May 29, 2002

  2. Projections based Block Recovery – Motivation • Conventional Algorithms use information of all surrounding area. • Using only highly correlated area

  3. Alternating Projections • Alternating Projections is projecting between two or more convex sets iteratively. Converging to a common point

  4. Projections based Block Recovery – Algorithm • 2 Steps • Pre Process : 1) Edge orientation detection 2) Surrounding vector extraction 3) Recovery vector extraction • Projections : 1) Projection operator P1 2) Projection operator P2 3) Projection operator P3

  5. Pre Process 1 –Edge Orientation Detection • Edge orientation in the surrounding area(S) of a missing block(M). In order to extend the geometric structure to the missing block. • Simple line masks at every i, j coordinate in surrounding area(S) of the missing block(M) for edge detection. Horizontal Line Mask Vertical Line Mask

  6. Pre Process 1 – Edge Orientation Detection • Responses of the line masks at window W : • Total magnitude of responses : • Th > Tv ; Horizontal line dominating area Th < Tv ; Vertical line dominating area

  7. Pre Process 2 – Surrounding Vectors • Surrounding Vectors, sk, are extracted from surrounding area of a missing block by N x N window. • Each vector has its own spatial and spectral characteristic. • The number of surrounding vectors, sk, is 8N.

  8. Pre Process 3 – Recovery Vector • Recovery vectors are extracted to restore missing pixels. • Two positions of recovery vectors are possible according to the edge orientation. • Recovery vectors consist of known pixels(white color) and missing pixels(gray color). • The number of recovery vectors, rk, is 2. Vertical line dominating area Horizontal line dominating area

  9. Projections based Block Recovery –Projection operator P1 • Recovery vectors, ri, for i = 1, 2 • Surrounding vectors, sj, for j = 1 ~ 8N • Surrounding vectors, S, form a convex hull in N2-dimensional space • Recovery vectors, R, are orthogonally projected onto the line defined by the closest surrounding vector, si, j : Projection Operator P1.

  10. Projections based Block Recovery –Projection operator P1 • Projection operator P1 : Convex hull (formed by surrounding vectors, containing information of local image structure)

  11. Projections based Block Recovery –Projection operator P1 • Surrounding vectors, sj, for j = 1 ~ 8N • Recovery vectors, ri, for i = 1, 2 • The closest vertex, sdi, from a recovery vector, ri. or equivalently in DCT domain, • P1 :

  12. Projections based Block Recovery –Projection operator P2 • Convex set C2 acts as an “identical middle”. • Projection operator P2 :

  13. Projections based Block Recovery – Projection operator P3 • Convex set C3 acts as a convex constraint between missing pixels and adjacent known pixels, (fN-1 fN). where, and is a N x N recovery vector in column vector form. fN-1 fN • Projection operator P3 :

  14. Projections based Block Recovery –Iterative Algorithm • Missing pixels in recovery vectors are restored by iterative algorithm of alternating projections : • N x N windows moving : Vertical line dominating area Horizontal line dominating area

  15. Projections based Block Recovery - Summary Edge Orientation Detection Surrounding Vector Extraction Recovery Vector Extraction Projection Operator P1 Projection Operator P2 Projection Operator P3 Iteration=I? All pixels?

  16. Simulation Results –Lena, 8 x 8 block loss Original Image Test Image

  17. Simulation Results –Lena, 8 x 8 block loss Ancis, PSNR = 28.68 dB Hemami, PSNR = 31.86 dB

  18. Simulation Results –Lena, 8 x 8 block loss Ziad, PSNR = 31.57 dB Proposed, PSNR = 34.65 dB

  19. Simulation Results –Lena, 8 x 8 block loss Ancis PSNR = 28.68 dB Hemami PSNR = 31.86 dB Ziad PSNR = 31.57 dB Proposed PSNR = 34.65 dB

  20. Simulation Results – Each Step Lena 8 x 8 block loss (a) (b) (c)

  21. Simulation Results –Peppers, 8 x 8 block loss Original Image Test Image

  22. Simulation Results – Peppers, 8 x 8 block loss Ancis, PSNR = 27.92 dB Hemami, PSNR = 31.83 dB

  23. Simulation Results – Peppers, 8 x 8 block loss Ziad, PSNR = 32.76 dB Proposed, PSNR = 34.20 dB

  24. Simulation Results –Lena, 8 x one row block loss Original Image Test Image

  25. Simulation Results –Lena, 8 x one row block loss Hemami, PSNR = 26.86 dB Proposed, PSNR = 30.18 dB

  26. Simulation Results –Masquerade, 8 x one row block loss Original Image Test Image

  27. Simulation Results –Masquerade, 8 x one row block loss Hemami, PSNR = 23.10 dB Proposed, PSNR = 25.09 dB

  28. Simulation Results –Lena, 16 x 16 block loss Original Image Test Image

  29. Simulation Results –Lena, 16 x 16 block loss Ziad, PSNR = 28.75 dB Proposed, PSNR = 32.70 dB

  30. Simulation Results –Foreman, 16 x 16 block loss Original Image Test Image Ziad, PSNR = 25.65 dB Proposed, PSNR = 30.34 dB

  31. Simulation Results –Flower Garden, 16 x 16 block loss Original Image Test Image Ziad, PSNR = 20.40 dB Proposed, PSNR = 22.62 dB

  32. Simulation Results – Test Data and Error • 512 x 512 “Lena”, “Masquerade”, “Peppers”, “Boat”, “Elaine”, “Couple” • 176 x 144 “Foreman” • 352 x 240 “Flower Garden” • 8 x 8 pixel block loss • 16 x 16 pixel block loss • 8 x 8 consecutive block losses • Peak Signal – Noise – Ratio

  33. Simulation Results – PSNR (8 x 8)

  34. Simulation Results – PSNR (Row, 16 x 16)

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