Accelerating Discrete Wavelet Transforms on GPUs
This work addresses performance bottlenecks in image-processing applications on GPUs, though it is incremental as it builds on existing lifting and convolution schemes.
The paper tackled the problem of accelerating 2D discrete wavelet transforms on GPUs by merging horizontal and vertical lifting parts into non-separable units, which halved the number of steps and reduced arithmetic operations, resulting in outperformance of existing schemes in many cases.
The two-dimensional discrete wavelet transform has a huge number of applications in image-processing techniques. Until now, several papers compared the performance of such transform on graphics processing units (GPUs). However, all of them only dealt with lifting and convolution computation schemes. In this paper, we show that corresponding horizontal and vertical lifting parts of the lifting scheme can be merged into non-separable lifting units, which halves the number of steps. We also discuss an optimization strategy leading to a reduction in the number of arithmetic operations. The schemes were assessed using the OpenCL and pixel shaders. The proposed non-separable lifting scheme outperforms the existing schemes in many cases, irrespective of its higher complexity.