Effects of different prediction schemes on lossless invisible watermarking

In view of high data embedding capacity and many real time applications, we have proposed a reversible invisible watermarking algorithm. The technique is used to embed a set of watermark data in an image using a one pass embedding process and later recovering the original image without any loss, after the extraction of watermark. Main motivation behind this work is the usage of two different prediction schemes-Weighted Causal Average and Context Based Image Compression Algorithm, to obtain two images similar to the original image, and using the error pattern resulted from the two predicted images to embed data depending upon the nature of image. Based on this error pattern, binary data is embedded in an image using two different embedding schemes- Bijective Mirror Mapping technique and Histogram Shifting Algorithm, resulting in better embedding capacity or payload capacity and better PSNR than other reversible one-pass watermarking algorithms mentioned in literature.