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Lossy cutset coding of bilevel images based on Markov random fields
Summary
A new lossy cutset coding method effectively compresses scenic bilevel images using a Markov random field model. This computationally simple technique preserves image structure and outperforms existing lossy and lossless compression methods.
Area of Science:
- Computer Science
- Image Processing
- Data Compression
Background:
- Bilevel image compression is crucial for efficient storage and transmission.
- Existing lossy methods often sacrifice essential image features.
- Lossless compression techniques can result in large file sizes.
Purpose of the Study:
- To introduce an effective, low-complexity lossy compression method for scenic bilevel images.
- To preserve key structural information, such as region borders.
- To achieve lower compression rates than lossless methods with minimal perceptual quality loss.
Main Methods:
- Proposed lossy cutset coding based on a Markov random field model.
- Lossless encoding of pixels along grid lines (cutset).
- MAP-based reconstruction of image block interiors from boundary pixels.
Main Results:
- The method preserves structural information like black-white region borders.
- Decoder reconstructs piecewise smooth images consistent with encoded pixels.
- MAP rule efficiently finds block interiors with minimal transitions, avoiding complex solutions.
- Computationally simple and outperforms current best lossy compression for scenic bilevel images.
- Achieves substantially lower rates than lossless techniques (e.g., JBIG) with little perceived quality loss.
Conclusions:
- Lossy cutset coding offers an effective and efficient solution for bilevel image compression.
- The method balances compression efficiency with the preservation of important image features.
- This approach provides a superior alternative to existing methods for scenic bilevel image compression.
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