Reversible information hiding is capable of fully reconstructing the original cover image while extracting the embedded secret information losslessly, making it a frequent choice for demanding fields such as medical diagnosis, military and remote sensing image processing. We propose a novel reversible information hiding method that embeds secret data into the Side Match Vector Quantization (SMVQ) compressed index table. Firstly, the original image is compressed through vector quantization (VQ), and then the compressed image is modified using SMVQ to obtain a transformed image, which is the SMVQ compressed index table. Finally, information hiding is performed based on the histogram distribution of the index table, resulting in a larger embedding capacity and a lower bit rate. Experimental results demonstrate that this method significantly increases the embedding capacity and further enhances the compression ratio.
In this paper, a reversible data hiding method aims at block truncation coding compressed color images is proposed, which can reconstruct the original compressed image effectively after extracting the embedded secret data. In order to improve the compression quality, a mean absolute deviation based clustering is applied to find an approximate optimal common bitmap. The common bitmap is used to get the quantization levels of each block in each color channel. The secret data are then embedded in the quantization levels of each block by the histogram shifting method and quantization level patterns based method. Experimental results show that the proposed method outperforms the existing reversible data hiding methods for BTC-compressed color images in terms of hiding capacity and visual quality.
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