Reversible information hiding algorithm based on classification of SMVQ indices

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.