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CIPR / RPI Homepage of Amir Said |
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Compound images are those like the example below, which contains
a combination of natural images (photos), text, and graphics. This
type of image occurs in many important applications, like document
imaging and printing [1-6].
Compound video is the equivalent for moving images, and is
commonly found in applications like remote visualization [5].
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The standard image and video compression methods that use linear
transforms (e.g., discrete cosine or wavelet) effectively exploit the
properties of the natural images and of the human visual system, and are
able to change the natural images considerably, while keeping an excellent
visual quality. The images containing text and graphics, on the other
hand, have edges that are harder to represent with the linear transforms,
and consequently many noticeable artifacts tend to appear around edges.
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What motivates the development of special compression for compound
content is the severity of this problem in the graphics and text regions.
The complex structure of the many text edges produces an accumulation
of artifacts around text, and the problem is made worse by the fact
that the visual quality expectations for text are very different from
those for natural images. People expect text to be crisp, and easily
perceive how artifacts reduce legibility and reading speed, and lower
the overall document quality.
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What seems to be the obvious solution to the problem is to use
high-level graphics languages. In reference [5] we
explain why, for a variety of practical reasons, in remote visualization
applications the current graphics languages are de facto much less
useful than expected, and compound image and video compression is a better
solution.
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It also seems that we can use the large variety of general image
and video segmentation methods for this application. However, the
sophisticated segmentation methods have a computational complexity
much higher than that needed for this problem. The segmentation needed
for compression can be much simpler [1,5,6], with a
slight increase in bit rate, but still maintaining good visual quality.
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An alternative to segmentation for compression is the decomposition
ot he image in layers, as in the MRC format and standards [2,4].
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For applications in which the image must fit a memory buffer, an
interesting problem is how to guarantee, in a single pass, a minimum
compression ratio [3].
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- [1] Amir Said and Alexander I. Drukarev,
- "Simplified segmentation for compound image compression,"
Proc. IEEE Int. Conf. Image Processing,
vol. 1, pp. 24-28, Oct. 1999. (PDF)
- [2] Debargha Mukherjee, Nasir Memon, and Amir Said,
- "JPEG-Matched MRC compression of compound documents,"
Proc. IEEE Int. Conf. Image Processing,
vol. 3, pp. 434-437, Oct. 2001. (PDF)
- [3] Debargha Mukherjee, Christos Chrysafis, and Amir Said,
- "Low complexity guaranteed fit compound document compression,"
Proc. IEEE Int. Conf. Image Processing,
vol. 1, pp. 225-228, Sept. 2002. (PDF)
- [4] Debargha Mukherjee, Christos Chrysafis, and Amir Said,
- "JPEG2000-Matched MRC compression of compound documents,"
Proc. IEEE Int. Conf. Image Processing,
vol. 3, pp. 73-76, Sept. 2002. (PDF)
- [5] Amir Said,
- "Compression of compound images and video for enabling rich media in embedded systems,"
SPIE/IS&T Visual Communications and Image Processing Conference,
SPIE Proc. vol. 5308, pp. 69-82, Jan. 2004. (Also HP Labs Report HPL-2004-89).
(PDF)
- [6] Amir Said,
- "Efficient and reliable dynamic quality control for compression of compound document images,"
Proc. IEEE Int. Conf. Image Processing,
Oct. 2004. (PDF)
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 Bio Page- Journal Page
- Arithmetic and Huffman Coding
- Structured Scalable Metaformats
- Virtual Environment Design Automation
- Set Partitioning in Hierarchical Trees
- SPIE/IS&T Conf. on Image and Video Communications and Processing
- Other Links
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