Image Compression
Contribution
Dr. Eugene Ageenko
Our contribution in the binary image compression field can be summarized to:
- EDM Storage System
-
New storage system architecture for engineering drawing and document images supporting instant preview, fast
decompression, and direct access to image fragments.
- Forward-adaptive modeling
-
New forward-adaptive modeling technique for the QM (and MQ) coding
algorithms aimed at the construction of a better initial model. This
alleviates the deficiency of coding caused by the learning cost problem when
image is split into small parts.
- Variable-size modeling
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New results and development work concerning variable-size context modeling, including the solution for the locality problem of the tree
splitting and a new space efficient two-stage tree
construction algorithm.
- Hybrid raster/vector image compression
(under Hybrid
Raster-Vector Data Processing Research)
-
New method for compression of the line-drawing images stored in
raster-graphic format in hybrid raster/vector storage systems. Compression
techniques utilize global image properties extracted using unsupervised
semantic feature detection algorithms such as raster-to-vector conversion and
Hough transform.
- Context-based image compression in parallel
-
Implementation of the fully sequential context-based image compression on a
parallel computer. We show by experiments that the proposed approach achieves
speedup that is proportional to the number of processors. The work efficiency
exceeds 50% with any reasonable number of processors.
- Map Image Storage System (MISS) (under
Real-time Imaging Research)
- We introduce a storage system for the map images that supports compact
storage size, decompression of partial image, and smooth transitions between
various scales. The main objective of the proposed storage system is to provide
map images for real-time applications that use portable devices with low memory
and computing resources. Compact storage size is achieved by dividing the maps
into binary layers, which are compressed using context-based statistical
modeling and arithmetic coding. Partial image decompression is supported by
tiling the image into blocks and implementing direct access to the compressed
blocks.
- Map Image
Compression (based on Morphological Reconstruction of Semantic Data)
-
The reconstruction technique is applied to the color layers of the raster map
images in order to restore the original semantic layers after the color
separation. The technique improves compression performance of the
reconstructed layers and provides also good visual quality of the
reconstructed image layers, and can therefore be applied for selective layer
removal/extraction in other map processing applications, e.g. area measurement
More details on contribution can be found in
PUBLICATIONS section
Document
Imaging