Reversible data embedding for vector quantization compressed images using search-order coding and index parity matching

Authors

  • Chuan Qin,

    1. School of Optical-Electrical and Computer Engineering, University of Shanghai for Science and Technology, Shanghai, China
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  • Chin-Chen Chang,

    Corresponding author
    1. Department of Information Engineering and Computer Science, Feng Chia University, Taichung, Taiwan
    2. Department of Computer Science and Information Engineering, Asia University, Taichung, Taiwan
    • Correspondence: Prof. Chin-Chen Chang, Department of Information Engineering and Computer Science, Feng Chia University, 100 Wenhwa Road, Taichung 40724, Taiwan.

      E-mail: alan3c@gmail.com

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  • Gwoboa Horng,

    1. Department of Computer Science and Engineering, National Chung Hsing University, Taichung, Taiwan
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  • Ying-Hsuan Huang,

    1. Department of Computer Science and Engineering, National Chung Hsing University, Taichung, Taiwan
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  • Yen-Chang Chen

    1. Department of Information Engineering and Computer Science, Feng Chia University, Taichung, Taiwan
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Abstract

Embedding secret data in vector quantization (VQ) compressed images with reversibility has been studied extensively in recent years. However, to date, the reported methods have not achieved satisfactory performances of hiding capacity and image compression ratio simultaneously. In this paper, we propose a reversible embedding method based on search-order coding (SOC) and index parity matching that can hide secret data into the compressed VQ index, that is, SOC index. If the parity of the candidate SOC index matches the current embedding bit and the error caused by SOC encoding is smaller than a pre-determined threshold, the length of the stego SOC index after embedding is significantly shorter than the original VQ index. On the receiver side, the embedded secret bits can be easily extracted by checking the parity of stego SOC indices, and all original VQ indices can be recovered correctly. Experimental results demonstrate that our method can achieve greater hiding capacity than the recently reported methods for the same image decompression quality. Copyright © 2014 John Wiley & Sons, Ltd.

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