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The Problem

Wavelet Domain Reconstruction of Lost Blocks in Wireless Image Transmission Shantanu Rane, Jeremiah Remus, Guillermo Sapiro Department of Electrical Engineering, University of Minnesota, Minneapolis. T. D. D. R. L. D. B. D. Examples. Classification of Lost (Code)Block. The Problem.

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The Problem

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  1. Wavelet Domain Reconstruction of Lost Blocks in Wireless Image Transmission Shantanu Rane, Jeremiah Remus, Guillermo Sapiro Department of Electrical Engineering, University of Minnesota, Minneapolis T D D R L D B D Examples Classification of Lost (Code)Block The Problem • Wavelet Decomposition of Image tile • Magnitude of wavelet coefficient indicates • Amount of change in image domain • Spatial location where this change occurs LL LH  LH HL HH HH HL • How to reconstruct lost wavelet coefficients in any • or all subbands ? • Compare coefficients of (code)blocks in 8-neighborhood • with threshold to classify into: • Edgy Selectively interpolate along edge direction • Non-Edgy  Interpolate from all T,L,B,R (possibly D) Applications  • Filling-in lost or masked areas in the wavelet domain • Compare with image-domain methods ~ nil or fewer iterations • Arbitrary shaped areas • ~ “wavelet-based inpainting” • JPEG2000 • ~ codeblocks (32x32, 64x64, etc) lost during • transmission Interpolation along Edge direction m n Reconstruction of JPEG2000 codeblocks Lost x1 x2 x • Problem  large code-blocks i.e. 32x32, 64x64 Y :Vector of inner pixels A: Matrix of outer pixels X : Vector of coefficients Get X as least squares solution • OK if code-blocks in HL,LH lost • If LL code-blocks lost, reconstruction not always possible if too many details are lost Lost Use X to get first inner layer Use outer two available layers to get X 32x32 blocks (all subbands lost) First Layer of Lost Block  Conclusions Vertical Edge • Fast multiscale error concealment algorithm • Applicable to reconstruction of lost JPEG2000 codeblocks • Not very good on diagonal edges and texture  Smooth surface • Problems for • Diagonal Edges • Textured Regions Key Ideas OK visual quality Very hard to restore Need edge direction Worst effect on visual quality Easier to restore LL LH LH HL HH HL HH Contains most edge energy Easy for perfectly vertical edge Hard for curved/inclined/fading edges Interpolation with smoothness constraint at boundaries of the mask [ Hemami, Meng, (1995) ]

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