Paper
1 May 1991 Sunset: a hardware-oriented algorithm for lossless compression of gray-scale images
Glen G. Langdon Jr.
Author Affiliations +
Abstract
Sunset is a lossless gray scale compression algorithm designed for a simple hardware implementation based on the prediction error context approach for predictive gray-scale compression. Sunset uses adaptive binary arithmetic coding with neighboring prediction error buckets as compact conditioning contexts for directly encoding the prediction error. A prototype card was built to send or receive either compressed or uncompressed images across the IBM PC/AT bus. A special interface was designed to load the memory buffer of a high resolution color display. The result is a component of a workstation prototype for radiologists and the physician who referred the patient. The Sunset approach handles gray scale images where the bits-per-pel precision is simply an input parameter to the algorithm; the compression algorithm itself is relatively insensitive to this parameter. For hardware simplicity, the error bucket (bin) identifier is determined by a leading-one detector (or priority encoder) circuit on the prediction error so the number of prediction error values per bucket is a power of two. The next less significant bits of the prediction error become the ''extra-bits'' which, when encoded, make the algorithm lossless. The number of extra-bits in a final (catch-all) bucket depends on the bits-per-pel parameter of the uncompressed image.
© (1991) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Glen G. Langdon Jr. "Sunset: a hardware-oriented algorithm for lossless compression of gray-scale images", Proc. SPIE 1444, Medical Imaging V: Image Capture, Formatting, and Display, (1 May 1991); https://doi.org/10.1117/12.45179
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CITATIONS
Cited by 19 scholarly publications and 48 patents.
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KEYWORDS
Binary data

Error analysis

Image compression

Computer programming

Prototyping

Statistical modeling

Data modeling

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