A Watermarking Algorithm for Three Dimensional Models Using Discrete Wavelet Transform

2012 ◽  
Author(s):  
Jiun-You Chen ◽  
Chen-Chung Liu ◽  
Pei-Chung Chung ◽  
Chun-Yuan Yu ◽  
Shyr-Shen Yu
Keyword(s):  
Wavelet Transform ◽  
Discrete Wavelet Transform ◽  
Three Dimensional ◽  
Discrete Wavelet ◽  
Dimensional Models ◽  
Three Dimensional Models

Mobile GPU Computing Based Filter Bank Convolution for Three-Dimensional Wavelet Transform

Biometrics ◽  
2017 ◽  
pp. 761-777
Author(s):  
Di Zhao
Keyword(s):  
Wavelet Transform ◽  
Discrete Wavelet Transform ◽  
Real Time ◽  
Gpu Computing ◽  
Three Dimensional ◽  
Discrete Wavelet ◽  
Computational Results ◽  
Real Time Processing ◽  
Time Processing ◽  
Mobile Gpu

Mobile GPU computing, or System on Chip with embedded GPU (SoC GPU), becomes in great demand recently. Since these SoCs are designed for mobile devices with real-time applications such as image processing and video processing, high-efficient implementations of wavelet transform are essential for these chips. In this paper, the author develops two SoC GPU based DWT: signal based parallelization for discrete wavelet transform (sDWT) and coefficient based parallelization for discrete wavelet transform (cDWT), and the author evaluates the performance of three-dimensional wavelet transform on SoC GPU Tegra K1. Computational results show that, SoC GPU based DWT is significantly faster than SoC CPU based DWT. Computational results also show that, sDWT can generally satisfy the requirement of real-time processing (30 frames per second) with the image sizes of 352×288, 480×320, 720×480 and 1280×720, while cDWT can only obtain read-time processing with small image sizes of 352×288 and 480×320.

Multi-Spectral Holographic Three-Dimensional Image Fusion Using Discrete Wavelet Transform

2006 ◽  
Vol 2 (4) ◽  
pp. 411-417 ◽  
Author(s):  
Bahram Javidi ◽  
Cuong Manh Do ◽  
Seung-Hyun Hong ◽  
Takanori Nomura
Keyword(s):  
Wavelet Transform ◽  
Image Fusion ◽  
Discrete Wavelet Transform ◽  
Three Dimensional ◽  
Discrete Wavelet ◽  
Dimensional Image

Mobile GPU Computing Based Filter Bank Convolution for Three-Dimensional Wavelet Transform

2016 ◽  
Vol 7 (2) ◽  
pp. 22-35
Author(s):  
Di Zhao
Keyword(s):  
Wavelet Transform ◽  
Discrete Wavelet Transform ◽  
Real Time ◽  
Gpu Computing ◽  
Three Dimensional ◽  
Discrete Wavelet ◽  
Computational Results ◽  
Time Processing ◽  
Mobile Gpu ◽  
High Efficient

Mobile GPU computing, or System on Chip with embedded GPU (SoC GPU), becomes in great demand recently. Since these SoCs are designed for mobile devices with real-time applications such as image processing and video processing, high-efficient implementations of wavelet transform are essential for these chips. In this paper, the author develops two SoC GPU based DWT: signal based parallelization for discrete wavelet transform (sDWT) and coefficient based parallelization for discrete wavelet transform (cDWT), and the author evaluates the performance of three-dimensional wavelet transform on SoC GPU Tegra K1. Computational results show that, SoC GPU based DWT is significantly faster than SoC CPU based DWT. Computational results also show that, sDWT can generally satisfy the requirement of real-time processing (30 frames per second) with the image sizes of 352×288, 480×320, 720×480 and 1280×720, while cDWT can only obtain read-time processing with small image sizes of 352×288 and 480×320.

scholarly journals A New Zero-Watermarking Algorithm Resisting Attacks Based on Differences Hashing

2016 ◽  
Vol 16 (2) ◽  
pp. 135-147 ◽  
Author(s):  
Baoru Han ◽  
Jingbing Li
Keyword(s):  
Wavelet Transform ◽  
Discrete Wavelet Transform ◽  
Discrete Cosine Transform ◽  
Three Dimensional ◽  
Discrete Wavelet ◽  
Volume Data ◽  
Cosine Transform ◽  
Binary Feature ◽  
Feature Sequence ◽  
Zero Watermarking

Abstract Medical volume data containing patient information is often faced with various attacks in the transmission process. In order to enhance the medical information system security, and effectively solve the problem of medical volume data protection, a new zero-watermarking algorithm is proposed in the paper. The new zero-watermarking algorithm takes advantage of three-dimensional discrete wavelet transform multi-resolution analysis characteristics of space and time, threedimensional discrete cosine transform properties, and differences hashing robust characteristic. In order to enhance watermarking algorithm security, Legendre chaotic neural network is used for scrambling original watermark image. The medical volume data is made by three-dimensional discrete wavelet transform, threedimensional discrete cosine transform and three-dimensional discrete inverse cosine transform r to obtain the medical volume data feature matrix (4×5×4), which is converted to 64-bit binary feature sequence through difference hashing algorithm. The 64-bit binary feature sequence is used to construct the zero-watermarking. The experimental results prove that the new zero-watermarking has favorable security and robustness resisting various attacks. Therefore, the new zero-watermarking algorithm is more applicable to protect medical volume data.

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