Digital Image Watermarking Using DWT and FWHT

Savakar, Dayanand G.; Pujar, Shivanand

doi:10.5815/ijigsp.2018.06.06

Cited by 12 publications

(4 citation statements)

References 8 publications

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“…Digital watermarking often utilizes common frequency transforms such as Fourier, Wavelet, Walsh-Hadamard, and discrete cosine transform. [7]. Khare et al [8], presented a security approach for telemedicine applications that utilized Homomorphic transform (HT), redundant discrete wavelet transforms (RDWT), and singular value decomposition (SVD).…”

Section: Related Workmentioning

confidence: 99%

An Optimized Medical Image Watermarking Approach for E-Health Applications

ABDI,

BOUKLI HACENE

2023

Med. Technol. J.

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Background: In recent years, information and communication technologies have been widely used in the healthcare sector. This development enables E-Health applications to transmit medical data, as well as their sharing and remote access by healthcare professionals. However, due to their sensitivity, medical data in general, and medical images in particular, are vulnerable to a variety of illegitimate attacks. Therefore, suitable security and effective protection are necessary during transmission. Method: In consideration of these challenges, we put forth a security system relying on digital watermarking with the aim of ensuring the integrity and authenticity of medical images. The proposed approach is based on Integer Wavelet Transform as an embedding algorithm; furthermore, Particles Swarm Optimization was employed to select the optimal scaling factor, which allows the system to be compatible with different medical imaging modalities. Results: The experimental results demonstrate that the method provides a high imperceptibility and robustness for both secret watermark and watermarked images. In addition, the proposed scheme performs better for medical images compared with similar watermarking algorithms. Conclusion: As it is suitable for a lossless-data application, IWT is the best choice for medical images integrity. Furthermore, using the PSO algorithm enables the algorithm to be compatible with different medical imaging modalities.

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Section: Related Workmentioning

confidence: 99%

An Optimized Medical Image Watermarking Approach for E-Health Applications

ABDI,

BOUKLI HACENE

2023

Med. Technol. J.

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“…Khose want to implement the AES algorithm in hardware in a way that reduces resource use without sacrificing throughput. The suggested system uses BRAM in place of standard S-box logic to provide results in real time [6].…”

Section: Review Of the Literaturementioning

confidence: 99%

Implementation of Digital Image Watermarking That Is Both Safe and Effective in Terms of Space on a Reconfigurable Platform

2021

Webology

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Now a day digital data is straightforward to handle yet it lets unauthorised users to obtain this information. In order to prevent unwanted access, typical methods include encryption and watermarking. In this research, we propose a Discrete Wavelet Transform (DWT)-based, cryptographically-integrated, invisible picture watermarking method for rapidly deploying new configurations of hardware. In DWT implementation, simpler equations are created for creation of approximation and detailed coefficients owing to which this operation requires only 306 slice registers and its maximum operating frequency is 556.174 MHz

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“…The compression ratio of the lossy compression scheme is very high. Lossy compression algorithms are generally based on orthogonal processes, such as the discrete cosine transform [3][4][5][6], the discrete Walsh transform [3], the Karhunen-Loeve transform [3,5,7], the discrete wavelet transform [1,2,4,5,8,9] and the Discrete Chebyshev Transform (DChT) [10][11][12][13]. All these transforms are unitary, symmetrical, reversible and the energy of the image before and after processing remains unchanged.…”

Section: Introductionmentioning

confidence: 99%

A Machine Learning Algorithm for Biomedical Images Compression Using Orthogonal Transforms

Kouanou¹,

Tchiotsop²,

Tchinda³

et al. 2018

IJIGSP

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Compression methods are increasingly used for medical images for efficient transmission and reduction of storage space. In this work, we proposed a compression scheme for colored biomedical image based on vector quantization and orthogonal transforms. The vector quantization relies on machine learning algorithm (K-Means and Splitting Method). Discrete Walsh Transform (DWaT) and Discrete Chebyshev Transform (DChT) are two orthogonal transforms considered. In a first step, the image is decomposed into sub-blocks, on each sub-block we applied the orthogonal transforms. Machine learning algorithm is used to calculate the centers of clusters and generates the codebook that is used for vector quantization on the transformed image. Huffman encoding is applied to the index resulting from the vector quantization. Parameters Such as Mean Square Error (MSE), Mean Average Error (MAE), PSNR (Peak Signal to Noise Ratio), compression ratio, compression and decompression time are analyzed. We observed that the proposed method achieves excellent performance in image quality with a reduction in storage space. Using the proposed method, we obtained a compression ratio greater than 99.50 percent. For some codebook size, we obtained a MSE and MAE equal to zero. A comparison between DWaT, DChT method and existing literature method is performed. The proposed method is really appropriate for biomedical images which cannot tolerate distortions of the reconstructed image because the slightest information on the image is important for diagnosis.

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Digital Image Watermarking Using DWT and FWHT

Cited by 12 publications

References 8 publications

An Optimized Medical Image Watermarking Approach for E-Health Applications

An Optimized Medical Image Watermarking Approach for E-Health Applications

Implementation of Digital Image Watermarking That Is Both Safe and Effective in Terms of Space on a Reconfigurable Platform

A Machine Learning Algorithm for Biomedical Images Compression Using Orthogonal Transforms

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