Lossless Data Embedding in BTC Codes Based on Prediction and Histogram Shifting

This paper proposes a lossless data embedding method for Block Truncation Coding (BTC) compressed images based on prediction and histogram shifting techniques. Because BTC is easy to implement, and requires significantly less CPU cost, it has arouse widely attention in applications where real-time processing is demanded. For the existing lossless data embedding method in BTC codes, the decoder has to be specifically designed so that the spatial domain image can perfectly reconstructed from the compressed codes. Therefore, the application of these methods is limited. In the proposed embedding method, we slightly modified the quantization values of BCT codes to embed data losslessly, and a standard BTC decoder can be used to decode the BTC codes. The embedded secret messages can be extracted at the receiver side with the correct key. The experimental results revealed that the proposed method not only recovers the stego image to its original one, but also preserved a high image quality.