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A PARALLELIZABLE LOSSLESS IMAGE COMPRESSION ALGORITHM FOR STANDARD AND PATHOLOGY IMAGES

Part of: Communication, information

Published online by Cambridge University Press:  23 January 2026

HONGFANG YUAN ,
SONGHAI DENG ,
XIANGKAI LIAN Open the ORCID record for XIANGKAI LIAN [Opens in a new window]  and
ZHENG’AN YAO
HONGFANG YUAN
Affiliation:
School of Mathematics, Sun Yat-sen University , Guangzhou, P. R. China; e-mail: yuanhf7@mail2.sysu.edu.cn, dengsh7@mail2.sysu.edu.cn, mcsyao@mail.sysu.edu.cn
SONGHAI DENG
Affiliation:
School of Mathematics, Sun Yat-sen University , Guangzhou, P. R. China; e-mail: yuanhf7@mail2.sysu.edu.cn, dengsh7@mail2.sysu.edu.cn, mcsyao@mail.sysu.edu.cn
XIANGKAI LIAN*
Affiliation:
College of Intelligence Science and Technology, National University of Defense Technology , Changsha, P. R. China
ZHENG’AN YAO
Affiliation:
School of Mathematics, Sun Yat-sen University , Guangzhou, P. R. China; e-mail: yuanhf7@mail2.sysu.edu.cn, dengsh7@mail2.sysu.edu.cn, mcsyao@mail.sysu.edu.cn
*
e-mail: lianxiangkai@nudt.edu.cn
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Abstract

This paper introduces a parallelizable lossless image compression algorithm designed for three-channel standard images and two-channel pathology images. The proposed algorithm builds on the Quite OK Image Format (QOI) by addressing its limitations in parallelizability and compression efficiency, thereby enhancing both the compression ratio and processing speed. By incorporating image context and optimizing pixel traversal sequences, the algorithm enables effective parallel processing, achieving rapid compression of million-pixel pathology images within milliseconds, and is scalable to larger whole-slide images. It also delivers exceptional performance in terms of both speed and compression ratio for standard images. Additionally, the low complexity lossless compression for images (LOCO-I) context prediction algorithm used in joint photographic experts group lossless standard (JPEG-LS) is parallelized to improve compression efficiency and speed. By implementing full-process parallelization across the entire compression workflow rather than confining parallelization to individual steps, this approach significantly enhances overall time performance.

Keywords

lossless image compressionparallelization

MSC classification

Primary: 68U10: Image processing
Secondary: 94A08: Image processing (compression, reconstruction, etc.)

Information

Type
Research Article
Information
The ANZIAM Journal , Volume 68 , 2026 , e1
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Copyright
© The Author(s), 2026. Published by Cambridge University Press on behalf of The Australian Mathematical Publishing Association Inc

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