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Title: | REVERSIBLE DATA HIDING USING HISTOGRAM MODIFICATION BY CONSIDERING THE HUMAN VISUAL SYSTEM |
Authors: | Sothuku, Naresh |
Keywords: | Digital watermarking;watermarking;human visual system;pixel |
Issue Date: | Jun-2013 |
Publisher: | I I T ROORKEE |
Abstract: | Digital watermarking.is a technique proposed for multimedia security applications like content protection and authentication. In digital watermarking, the watermark is embedded into a multimedia data (cover data), in such a way that distortion of the cover data due to watermarking is almost imperceptible. In addition, in reversible watermarking the cover data is restored after the watermark extraction. Reversible watermarking finds widespread use in military and medical applications, where distortion—free recovery of the original data after watermark extraction is of utmost importance. This thesis provides an overview of the state-ofthe- art reversible watermarking techniques. The most popular approach in reversible watermarking is based on the histogram modification techniques. The present reversible watermarking algorithms do not exploit the human visual system (HVS) properties effectively. We propose two algorithms for reversible watermarking based on histogram modification, which use better HVS models during embedding. The first method is a texture based method, where JND computation is redefined to include the texture information. The second is surround pixel prediction based method. It uses all surround pixels in a non-causal window for predicting the value of a pixel. The dissertation describes the proposed methods after explaining some histogram modification based methods. Simulation results show that the proposed algorithms have better performance over previous histogram modification based reversible watermarking techniques. |
URI: | http://localhost:8081/jspui/handle/123456789/16094 |
metadata.dc.type: | Other |
Appears in Collections: | MASTERS' THESES (E & C) |
Files in This Item:
File | Description | Size | Format | |
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G22257.pdf | 7.46 MB | Adobe PDF | View/Open |
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