Information Embedding Ensuring the Complete Recoverability of Cover Data

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This study throws light on the relationship between lossless data compression and lossless data embedding. Lossless data embedding is a watermarking technique that is capable of recovering the cover data after the embedded data has been extracted. We establish an information-theoretic model for a one-dimensional formalization of lossless generalized LSB-embedding methods. Based on the model, we first consider the problem of minimizing the Length of the cover data required to embed a watermark, and design a universal embedding method that can asymptotically attain the minimum cover data length. We then develop an LZ77 variation that performs neither compression nor expansion. Instead, it embeds as much extra information as possible while keeping the input file size. Asymptotic embedding capacity is then derived on the proposed variation, which shows a duality relation between data compression and lossless data embedding.