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Explorations in Image Partition Encoding

Explorations in Image Partition Encoding. Sameer Agarwal Department of Computer Science and Engineering University of California, San Diego. Background. Multiple Raster Content Specification Three layered image structure Aimed at documents with a distinct text layer.

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Explorations in Image Partition Encoding

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  1. Explorations in Image Partition Encoding Sameer Agarwal Department of Computer Science and Engineering University of California, San Diego

  2. Background • Multiple Raster Content Specification • Three layered image structure • Aimed at documents with a distinct text layer. • Layer specific compression algorithms for better compression • DjVu from At&T is a working system which implements a scheme like this.

  3. Generalization to natural images • Implement the MRC idea to natural scenes/ video. • Use a robust image segmentation method (e.g. N-Cut) to break the image into pieces. • Compress each segment separately. • Many different ways of compressing the segments possible including, texture modelling and wavelet based compression.

  4. How do you reassemble an image from partitions ? • Besides the individuals segments a image partition map needs to be stored for re-assembly of the image. How do you efficiently store the image partition map ?

  5. The shape coding problem • Shape descriptors like a fourier transform of the curve. • Chain-coding of the boundary. • Bitmap compression • Label each region with a small integer and compress the resulting low bitrate image.

  6. The Naïve solution Label each segment by its segment number. Problem: The number of bits per pixel depends on the number of segments. (Press Enter for the smart solution)

  7. The four color theorem

  8. The smart solution • The four color theorem allows you to encode an image partition with just 4 colors. • A strict upper bound of 2 bpp. Problem: How to four color ? How to find the most compressible 4-coloring ?

  9. Explorations in Four-coloring • Polynomial time algorithms exist but are quite useless • Alternate solutions • Integer -> Linear programming based solutions • Heuristic based coloring • Backtracking • First two methods do not guarantee a 4 coloring. • Backtracking is exponential. • None of them solve the most-compressible 4-coloring problem.

  10. Four-coloring Our attempts : Greedy backtracking • Greedy on color use, hence tries to minimize color-entropy • Inefficient and does not get the best coloring, random does better sometimes.

  11. Attempts (contd.) Spectral coloring • Approximate coloring method, based on using the top two eigenvectors of the laplacian. • Gives approximate coloring, but makes mistakes. Probably these mistakes can be repaired. • work still in progress..

  12. Attempts (contd.) Balkanization • Coloring becomes difficult with increasing connectivity of the graph. • Break segments with very high degree into pieces with lower degree. • The size of the graph increases too fast for the backtracking based methods. • The spectral methods do not show any difference.

  13. Acknowledgements • Serge for all the hours. • Pam Cosman and Yan Ye for their JBIG2 encoder. and the music of Nickelback for keeping me company.

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