Parallel lossless image compression using Huffman and arithmetic coding

We show that high-resolution images can be encoded and decoded efficiently in parallel. We present an algorithm based on the hierarchical MLP method, used either with Huffman coding or with a new variant of arithmetic coding called quasi-arithmetic coding. The coding step can be parallelized, even t...

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Bibliographic Details
Published in:Information processing letters Vol. 59; no. 2; pp. 65 - 73
Main Authors: Howard, Paul G., Vitter, Jeffrey Scott
Format: Journal Article
Language:English
Published: Amsterdam Elsevier B.V 22.07.1996
Elsevier Science
Elsevier Sequoia S.A
Subjects:
Algorithmics. Computability. Computer arithmetics
Algorithms
Applied sciences
Arithmetic coding
Artificial intelligence
Computer science; control theory; systems
Data compression
Exact sciences and technology
Huffman coding
Information processing
Parallel algorithms
Parallel processing
Pattern recognition. Digital image processing. Computational geometry
Studies
Theoretical computing
Arithmetic coding
Huffman coding
Parallel algorithms
Data compression
High resolution
Image processing
Coding
Huffman code
Prediction
Arithmetic code
ISSN:0020-0190, 1872-6119
Online Access:Get full text
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Summary:We show that high-resolution images can be encoded and decoded efficiently in parallel. We present an algorithm based on the hierarchical MLP method, used either with Huffman coding or with a new variant of arithmetic coding called quasi-arithmetic coding. The coding step can be parallelized, even though the codes for different pixels are of different lengths; parallelization of the prediction and error modeling components is straightforward.
Bibliography:ObjectType-Article-1
SourceType-Scholarly Journals-1
content type line 14
ISSN:0020-0190
1872-6119
DOI:10.1016/0020-0190(96)00090-7