NDLI: Blind image watermarking method based on linear canonical wavelet transform and QR decomposition

Content Provider	IET Digital Library
Author	Guo, Yong Li, Bing Zhao
Abstract	Inspired by the fact that wavelet transform can be written as a classical convolution form, a new linear canonical wavelet transform (LCWT) based on generalised convolution theorem associated with linear canonical transform (LCT) is proposed recently. The LCWT not only inherits the advantages of multi-resolution analysis of wavelet transform (WT), but also has the capability of image representations in the LCT domain. Based on these good properties, the authors propose a novel image watermarking method using LCWT and QR decomposition. Compared with the existing image watermarking methods based on discrete WT or QR, this novel image watermarking method provides more flexibility in the image watermarking. Peak signal-to-noise ratio, normalised cross and structural similarity index measure are used to verify the advantages of the proposed method in simulation experiments. The experiment results show that the proposed method is not only feasible, but also robust to some geometry attacks and image processing attacks.
Starting Page	773
Ending Page	786
Page Count	14
ISSN	17519659
Volume Number	10
e-ISSN	17519667
Issue Number	Issue 10, Oct (2016)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/iet-ipr/10/10
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/iet-ipr.2015.0818
Journal	IET Image Processing
Publisher Date	2016-10-01
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	Blind Image Watermarking Computer Vision And Image Processing Technique Generalised Convolution Theorem Geometry Attack Image And Video Coding Image Processing Attack Image Representations Image Watermarking Integral Transforms Linear Canonical Transform Linear Canonical Wavelet Transform Multi-resolution Analysis Peak Signal-to-noise Ratio QR Decomposition Wavelet Transform
Content Type	Text
Resource Type	Article
Subject	Signal Processing Electrical and Electronic Engineering Computer Vision and Pattern Recognition Software

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