Rewritable Data Embedding in Image based on Improved Coefficient Recovery

Nowadays, most images are stored and transmitted in certain compressed forms based on some coding standards. Usually, the image is transformed, e.g., by discrete cosine transformation, and hence coefficient makes up a large proportion of the compressed bit stream. However, these coefficients might be corrupted or completely lost due to transmission errors or damages incurred on the storage device. Therefore, in this work, we aim to improve a conventional coefficient recovery method. Specifically, instead of using the Otsu’s method adopted in the conventional method, an adaptive segmentation method is utilized to split the image into background and foreground regions, forming non-overlapping patches. Missing coefficients in these non-overlapping patches are recovered independently. In addition, a rewritable data embedding method is put forward by judiciously selecting patches to embed data. Experiments are carried to verify the basic performance of the proposed methods. In the best-case scenario, an improvement of 31.32% in terms of CPU time is observed, while up to 7149 bits of external data can be embedded into the image.