Lossless Data Hiding in VQ Compressed Images Using Adaptive Prediction Difference Coding

Abstract

Data hiding in digital images is an important cover communication technique. This paper studies the lossless data hiding in an image compression domain. We present a novel lossless data hiding scheme in vector quantization (VQ) compressed images using adaptive prediction difference coding. A modified adaptive index rearrangement (AIR) is presented to rearrange a codebook, and thus to enhance the correlation of the adjacent indices in the index tables of cover images. Then, a predictor based on the improved median edge detection is used to predict the indices by retaining the first index. The prediction differences are calculated using the exclusive OR (XOR) operation, and the vacancy capacity of each prediction difference type is evaluated. An adaptive prediction difference coding method based on the vacancy capacities of the prediction difference types is presented to encode the prediction difference table. Therefore, the original index table is compressed, and the secret data are embedded into the vacated room. The experimental results demonstrate that the proposed scheme can reduce the pure compression rate compared with the related works.