Integer Discrete Cosine Transform via Lossless Walsh-Hadamard Transform with Structural Regularity for Low-Bit-Word-Length

2010 ◽  
Vol E93-A (4) ◽  
pp. 734-741 ◽  
Author(s):  
Taizo SUZUKI ◽  
Masaaki IKEHARA
Keyword(s):  
Discrete Cosine Transform ◽  
Word Length ◽  
Hadamard Transform ◽  
Cosine Transform ◽  
Structural Regularity ◽  
Walsh Hadamard Transform

LWT-DCT based Image Watermarking Scheme using Normalized SVD

2020 ◽  
Vol 13 ◽  
Author(s):  
Rahul Dixit ◽  
Amita Nandal ◽  
Arvind Dhaka ◽  
Vardan Agarwal ◽  
Yohan Varghese
Keyword(s):  
Wavelet Transform ◽  
Singular Value Decomposition ◽  
Discrete Cosine Transform ◽  
Image Watermarking ◽  
Singular Value ◽  
Cover Image ◽  
Lifting Wavelet Transform ◽  
Cosine Transform ◽  
Lifting Wavelet ◽  
Value Decomposition

Background: Nowadays information security is one of the biggest issues of social networks. The multimedia data can be tampered with, and the attackers can then claim its ownership. Image watermarking is a technique that is used for copyright protection and authentication of multimedia. Objective: We aim to create a new and more robust image watermarking technique to prevent illegal copying, editing and distribution of media. Method : The watermarking technique proposed in this paper is non-blind and employs Lifting Wavelet Transform on the cover image to decompose the image into four coefficient matrices. Then Discrete Cosine Transform is applied which separates a selected coefficient matrix into different frequencies and later Singular Value Decomposition is applied. Singular Value Decomposition is also applied to the watermarking image and it is added to the singular matrix of the cover image which is then normalized followed by the inverse Singular Value Decomposition, inverse Discrete Cosine Transform and inverse Lifting Wavelet Transform respectively to obtain an embedded image. Normalization is proposed as an alternative to the traditional scaling factor. Results: Our technique is tested against attacks like rotation, resizing, cropping, noise addition and filtering. The performance comparison is evaluated based on Peak Signal to Noise Ratio, Structural Similarity Index Measure, and Normalized Cross-Correlation. Conclusion: The experimental results prove that the proposed method performs better than other state-of-the-art techniques and can be used to protect multimedia ownership.

Fast odd discrete cosine transform algorithms

1990 ◽  
Vol 26 (8) ◽  
pp. 503
Author(s):  
S.C. Chan ◽  
K.L. Ho
Keyword(s):  
Discrete Cosine Transform ◽  
Cosine Transform
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