Adaptive Directional Lifting Structure of Three Dimensional Non-Separable Discrete Wavelet Transform for High Resolution Volumetric Data Compression

2016 ◽  
Vol E99.A (5) ◽  
pp. 892-899 ◽  
Author(s):  
Fairoza Amira BINTI HAMZAH ◽  
Taichi YOSHIDA ◽  
Masahiro IWAHASHI ◽  
Hitoshi KIYA
Keyword(s):  
Wavelet Transform ◽  
High Resolution ◽  
Data Compression ◽  
Discrete Wavelet Transform ◽  
Three Dimensional ◽  
Discrete Wavelet ◽  
Volumetric Data

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.

Sign in / Sign up

Export Citation Format

Share Document