Reversible Watermarking Algorithm Using Sorting and Prediction

• Published in: IEEE transactions on circuits and systems for video technology Vol. 19; no. 7; pp. 989 - 999
• Main Authors: Sachnev, V., Hyoung Joong Kim, Jeho Nam, Suresh, S., Yun Qing Shi
• Format: Journal Article
• Language: English
• Published: New York, NY IEEE 01.07.2009; Institute of Electrical and Electronics Engineers; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Applied sciences; Circuits; Cryptography; Data encapsulation; Data mining; Decoding; Detection, estimation, filtering, equalization, prediction; Distortion; Exact sciences and technology; Image quality; Image resolution; Information security; Information, signal and communications theory; Lossless data hiding; Permissible error; Pixel; Position (location); Prediction algorithms; prediction error expansion; reversible watermarking; Signal and communications theory; Signal, noise; Sorting; Telecommunications and information theory; Watermarking; Performance evaluation; Image quality; Lossless circuit; Information protection; Lossless data hiding; prediction error expansion; reversible watermarking; Miniaturization; Localization; Algorithm; Signal distortion; Digital watermarking
• ISSN: 1051-8215; 1558-2205
• DOI: 10.1109/TCSVT.2009.2020257

This paper presents a reversible or lossless watermarking algorithm for images without using a location map in most cases. This algorithm employs prediction errors to embed data into an image. A sorting technique is used to record the prediction errors based on magnitude of its local variance. Using sorted prediction errors and, if needed, though rarely, a reduced size location map allows us to embed more data into the image with less distortion. The performance of the proposed reversible watermarking scheme is evaluated using different images and compared with four methods: those of Kamstra and Heijmans, Thodi and Rodriguez, and Lee et al. The results clearly indicate that the proposed scheme can embed more data with less distortion.