Parallel Implementation of the 2D Discrete Wavelet Transform on Graphics Processing Units: Filter Bank versus Lifting

Abstract

Abstract-The widespread usage of the discrete wavelet transform (DWT) has motivated the development of fast DWT algorithms and their tuning on all sorts of computer systems. Several studies have compared the performance of the most popular schemes, known as Filter Bank Scheme (FBS) and Lifting Scheme (LS), and have always concluded that LS is the most efficient option. However, there is no such study on streaming processors such as modern Graphics Processing Units (GPUs). Current trends have transformed these devices into powerful stream processors with enough flexibility to perform intensive and complex floating-point calculations. The opportunities opened up by these platforms, as well as the growing popularity of the DWT within the computer graphics field, make a new performance comparison of great practical interest. Our study indicates that FBS outperforms LS in current-generation GPUs. In our experiments, the actual FBS gains range between 10 percent and 140 percent, depending on the problem size and the type and length of the wavelet filter. Moreover, design trends suggest higher gains in future-generation GPUs.

GPU-based 3D lower tree wavelet video encoder

“…A reasonable speed-up (13) has been obtained with high video resolutions. However, we can achieve better performance if we compute the filtering steps from the shared memory.…”

“…In [12], a Single Instruction Multiple Data (SIMD) algorithm runs the 2D-DWT on a GeForce 7800 GTX using Cg and OpenGL, with a remarkable speed-up. A similar effort has been performed in [13] combining Cg and the 7800 GTX to report a 1.2-3.4 speed-up versus a CPU counterpart. http://asp.eurasipjournals.com/content/2013/1/24…”

Parallel wavelet schemes for images

“…The row-column methods process all of the horizontal filtering steps prior to the vertical ones. The row-column method applied on the entire 2-D image was used for instance in [10][11][12][13][14][15][16]. In some papers, the transition between the horizontal and vertical stage is accompanied with data transposition.…”

Massively parallel non-stationary EEG data processing on GPGPU platforms with Morlet continuous wavelet transform

“…For instance, Wong et al implement a two-dimension DWT with Cg and OpenGL on a GeForce GTX 7800 [31]. Similarly, in [28] authors also explore the implementation of a fast 2D-DWT with Filter Bank Scheme (FBS) and Lifting Scheme (LS) using Cg on the same GPU. With NVIDIA's CUDA library [6], people have implemented 2D-DWT variants [10,18] and a 3D-DWT on GPUs [11].…”