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Radiological image compression using error-free irreversible two-dimensional direct-cosine-transform coding

H K Huang, S C Lo, B K Ho

    Journal of the Optical Society of America. A, Optics and Image Science
    |May 1, 1987
    PubMed
    Summary
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    See all related articles

    This study explores image compression for radiological images using error-free and irreversible techniques. Irreversible two-dimensional direct-cosine-transform (2D-DCT) coding, particularly Full-Frame Bit Allocation (FFBA), offers higher compression ratios than error-free methods.

    Area of Science:

    • Medical Imaging
    • Computer Science
    • Signal Processing

    Background:

    • Radiological image compression is crucial for efficient storage and transmission.
    • Existing error-free methods offer limited compression ratios (2:1 to 3:1).
    • Irreversible techniques, like 2D-DCT, promise higher compression but can introduce artifacts.

    Purpose of the Study:

    • To discuss and compare error-free and irreversible image compression techniques for radiological images.
    • To detail block-quantization and Full-Frame Bit Allocation (FFBA) methods within 2D-DCT coding.
    • To present the design of the FFBA technique and its advantages.

    Main Methods:

    • Description of run-length and Huffman coding for error-free compression.
    • Explanation of block-quantization and FFBA for irreversible 2D-DCT coding.

    Related Experiment Videos

  • Design and analysis of the FFBA technique for radiological image compression.

    • Main Results:

    • Error-free compression achieves ratios of 2:1 to 3:1.
    • Irreversible 2D-DCT coding, especially FFBA, achieves significantly higher compression ratios.
    • FFBA avoids block artifacts seen with block-quantization at similar or higher compression ratios.
    • Combining FFBA with Huffman coding yields even greater compression.

    Conclusions:

    • Irreversible 2D-DCT coding, particularly FFBA, is superior for achieving high compression ratios in radiological images.
    • FFBA offers a viable alternative to block-quantization, mitigating artifact issues.
    • Further optimization is possible by combining FFBA with entropy coding methods like Huffman coding.