Research Document Imaging Image Compression

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Binary (multi-level) Image Compression
Dr. Eugene Ageenko et al.

Content

1. Previous Work
2. Context-based Image Compression and JBIG
3. Our Contribution
4. References

Every document imaging application has a raster image as the basic component. A few color tones are usually sufficient to represent the original document, and only two tones are widely used. A multi-color document can be decomposed into bi-level planes, and be processed as the collection of bi-level images.

The storage size of the images has been a major restriction in digital imaging systems for decades. Existing image compression methods do not provide an efficient universal solution, nor satisfy all application-specific requirements, such as on-line retrieval, high quality restoration, spatial access and intellectual post-processing. Better solutions must therefore be developed.

The storage size impacts on nearly every aspect of a digital imaging system. The necessity of compression for saving storage space is, therefore, obvious. Cost savings emerge from several areas: fewer storage resources are needed and less network bandwidth required. Faster transfer implies a productivity gain because it makes Internet and LAN access more useful; less time is spent in waiting, and fewer resources are required to retrieve the files.