Reducing Share Size in JPEG Image Sharing Over GF (2 <sup> <i>m</i> </sup> ) and GF ( <i>p</i> ) Galois Fields

Image sharing is a crucial security technique of protecting the original image. Most image sharing methods elaborate on the uncompressed formats and cannot be applied directly to compressed formats such as JPEG format. Recently, some related JPEG sharing methods were proposed. However, these methods are short in size reduction. To address this issue, we introduce a novel JPEG image sharing method that significantly reduces the file sizes of shared images while maintaining compatibility with the JPEG standard. Specifically, an original JPEG bitstream is parsed to obtain three components, namely, DC appended bits (DCAs), AC Huffman codes (ACHs), and AC appended bits (ACAs). Different sharing mechanisms are applied for these three components. The DCAs are shared over GF(2m) in a way that prevents overflow in the shared DC coefficients. The ACH sharing is performed by polynomials over GF(p) with an intermediate process by Huffman table mapping and the ACAs are shared by polynomials over GF(2m) with directed codes. Consequently, the ACH and ACA shares with smaller sizes are generated, respectively. To be compatible with the JPEG standard, the shared ACAs are adaptively modulated according to the shared ACHs. Finally, the shared JPEG images can be reconstructed by the shared DCAs, ACHs, ACAs and the original partial JPEG information. The experimental results demonstrate that the proposed method can efficiently reduce the file sizes of shared JPEG images and preserve the JPEG format for each shared image.

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Publication Details

Journal
IEEE Internet of Things Journal
Published
2025-06-09
DOI
https://doi.org/10.1109/jiot.2025.3574532
Primary Topic
Medical Image Segmentation Techniques
Type
article
Field-Weighted Citation Impact
0.00

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article

Reducing Share Size in JPEG Image Sharing Over GF (2 m ) and GF ( p ) Galois Fields

Chunqiang Yu, Ching‐Nung Yang, Xianquan Zhang, Pengfei Wang et al.
IEEE Internet of Things Journal
Medical Image Segmentation Techniques
article

Reducing Share Size in JPEG Image Sharing Over GF (2 m ) and GF ( p ) Galois Fields

Chunqiang Yu, Ching‐Nung Yang, Xianquan Zhang, Pengfei Wang, S.-L. Cheng
article en

Abstract

Image sharing is a crucial security technique of protecting the original image. Most image sharing methods elaborate on the uncompressed formats and cannot be applied directly to compressed formats such as JPEG format. Recently, some related JPEG sharing methods were proposed. However, these methods are short in size reduction. To address this issue, we introduce a novel JPEG image sharing method that significantly reduces the file sizes of shared images while maintaining compatibility with the JPEG standard. Specifically, an original JPEG bitstream is parsed to obtain three components, namely, DC appended bits (DCAs), AC Huffman codes (ACHs), and AC appended bits (ACAs). Different sharing mechanisms are applied for these three components. The DCAs are shared over GF(2m) in a way that prevents overflow in the shared DC coefficients. The ACH sharing is performed by polynomials over GF(p) with an intermediate process by Huffman table mapping and the ACAs are shared by polynomials over GF(2m) with directed codes. Consequently, the ACH and ACA shares with smaller sizes are generated, respectively. To be compatible with the JPEG standard, the shared ACAs are adaptively modulated according to the shared ACHs. Finally, the shared JPEG images can be reconstructed by the shared DCAs, ACHs, ACAs and the original partial JPEG information. The experimental results demonstrate that the proposed method can efficiently reduce the file sizes of shared JPEG images and preserve the JPEG format for each shared image.

IEEE Internet of Things JournalVol. 12(15)
Nanyang Technological University (SG), Guangxi Normal University (CN), National Dong Hwa University (TW)
Natural Science Foundation of Guangxi Zhuang Autonomous Region, National Natural Science Foundation of China
Openalex Percentile: Top 89%
Medical Image Segmentation Techniques
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