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Document Imaging Dr. Eugene Ageenko et al.

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Contents

1. Background
2. Binary (multi-level) Image Compression
3. Document Image Restoration and Analysis
4. Hybrid Raster-Vector Data Processing
5. Storage System for Document Imaging and Engineering Document Management

Publications

References

1. Background in Document Imaging

2. Binary (multi-level) Image Compression

The technology that conceives the better statistical modeling methods featuring novel variable-size and multi-layer context approaches. Improvement in the compression would also give greater freedom in the design of the other features such as spatial access and raster/vector hybridization.

3. Document Image Restoration and Analysis

Advanced image restoration techniques aimed at improving the image quality after the digitization by filtering and noise removal. Image restoration reduces the number of features in the image preserving the image content, which improves the compression performance as well. Better methods should be designed by jointly taking into account both aims: restoration and compression.

4. Hybrid Raster-Vector Data Processing

The higher-level modeling methods, such as raster-to-vector conversion and image analysis, used to extract the semantic information from digitized engineering and map images. This information is used for improved hybrid raster/vector image editing and processing, including:

• improved statistical modeling, and consequently compression performance;
• image scaling with selective feature preservation;
• quality restoration by feature-based image filtering.

5. Storage System for DI and EDM

A storage system architecture supporting

• small storage space
• lossless reconstruction
• instant preview
• fast decompression
• spatial access that is direct access to image fragments

Further information can be found in PhD Thesis "Document Image Compression", see Publications.