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High-Capacity Reversible Data Hiding in Encrypted Images Based on Adaptive Predictor and Compression of Prediction Errors

Author

Listed:
  • Bin Huang

    (Computer Engineering College, Jimei University, Xiamen 361021, China)

  • Chun Wan

    (Computer Engineering College, Jimei University, Xiamen 361021, China)

  • Kaimeng Chen

    (Computer Engineering College, Jimei University, Xiamen 361021, China)

Abstract

Reversible data hiding in encrypted images (RDHEI) is a technology which embeds secret data into encrypted images in a reversible way. In this paper, we proposed a novel high-capacity RDHEI method which is based on the compression of prediction errors. Before image encryption, an adaptive linear regression predictor is trained from the original image. Then, the predictor is used to obtain the prediction errors of the pixels in the original image, and the prediction errors are compressed by Huffman coding. The compressed prediction errors are used to vacate additional room with no loss. After image encryption, the vacated room is reserved for data embedding. The receiver can extract the secret data and recover the image with no errors. Compared with existing approaches, the proposed method efficiently improves the embedding capacity.

Suggested Citation

  • Bin Huang & Chun Wan & Kaimeng Chen, 2021. "High-Capacity Reversible Data Hiding in Encrypted Images Based on Adaptive Predictor and Compression of Prediction Errors," Mathematics, MDPI, vol. 9(17), pages 1-15, September.
    • Handle: RePEc:gam:jmathe:v:9:y:2021:i:17:p:2166-:d:629353
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    References listed on IDEAS

    as
    1. Xi-Yan Li & Xia-Bing Zhou & Qing-Lei Zhou & Shi-Jing Han & Zheng Liu, 2020. "High-Capacity Reversible Data Hiding in Encrypted Images by Information Preprocessing," Complexity, Hindawi, vol. 2020, pages 1-12, October.
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    Cited by:

    1. Fang Ren & Ziyi Wu & Yaqi Xue & Yanli Hao, 2023. "Reversible Data Hiding in Encrypted Image Based on Bit-Plane Redundancy of Prediction Error," Mathematics, MDPI, vol. 11(11), pages 1-19, May.

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