JOURNAL ARTICLE

Development of Low-Complexity Video Watermarking With Conjugate Symmetric Sequency–Complex Hadamard Transform

K. MeenakshiK. Satya PrasadCh. Srinivasa Rao

Year: 2017 Journal:   IEEE Communications Letters Vol: 21 (8)Pages: 1779-1782   Publisher: IEEE Communications Society

Abstract

In this letter, a blind video watermarking in phase domain is proposed with conjugate symmetric sequency-complex hadamard transform (CS-SCHT). The watermark is made imperceptible and robust using methods, such as low-amplitude block selection and amplitude boost. Simulations are conducted to perform objective and subjective evaluation on watermark imperceptibility and robustness against attacks, such as high efficiency video coding compression, rescaling, and cropping. Performance metrics, such as peak signal to noise ratio (PSNR), mean opinion score (MOS), and bit error rate (BER), are used, and a comparison is carried out with discrete Fourier transform (DFT). It has been shown that CS-SCHT offers comparable performance with DFT in terms of PSNR, MOS, and BER. As this transform require only few multiplications for computation of the transform kernel, this scheme has 37.5% significant saving on hardware for high definition videos compared with DFT.

Keywords:
Watermark Digital watermarking Hadamard transform Algorithm Peak signal-to-noise ratio Computer science Discrete cosine transform Discrete Fourier transform (general) Robustness (evolution) Mathematics Discrete Hartley transform Transform coding Computer vision Fourier transform Fractional Fourier transform Image (mathematics) Fourier analysis

Metrics

23
Cited By
1.53
FWCI (Field Weighted Citation Impact)
7
Refs
0.86
Citation Normalized Percentile
Is in top 1%
Is in top 10%

Citation History

Topics

Advanced Steganography and Watermarking Techniques
Physical Sciences →  Computer Science →  Computer Vision and Pattern Recognition
Video Coding and Compression Technologies
Physical Sciences →  Computer Science →  Signal Processing
Digital Media Forensic Detection
Physical Sciences →  Computer Science →  Computer Vision and Pattern Recognition

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