An SMVQ compressed data hiding scheme based on multiple linear regression prediction
Heng-Xiao Chi, Chin‐Chen Chang, Yanjun Liu · Connection Science · 2020
In this paper, we propose a side matching vector quantisation (SMVQ) data hiding scheme for image using multiple linear regression prediction. For each pixel block, the proposed scheme combines the multiple linear regression algorithm and the SMVQ algorithm, so that it can more accurately match the codeword or directly obtain the predicted value closer to the real pixel. Our experimental results show that when the VQ codebook sizes are 128, 256, 512, and 1024, and the SCB size is 16, this scheme obtains a better compression rate and information embedding ability. It can be concluded from the experimental results that this scheme is superior to existing algorithms in terms of compression and embedding capacity.