Title :
A fast mixed-band lifting wavelet transform on the GPU
Author :
Tran Minh Quan ; Won-Ki Jeong
Author_Institution :
Sch. of Electr. & Comput. Eng., Ulsan Nat. Inst. of Sci. & Technol., Ulsan, South Korea
Abstract :
Discrete wavelet transform (DWT) has been widely used in many image compression applications, such as JPEG2000 and compressive sensing MRI. Even though a lifting scheme [1] has been widely adopted to accelerate DWT, only a handful of research has been done on its efficient implementation on many-core accelerators, such as graphics processing units (GPUs). Moreover, we observe that rearranging the spatial locations of wavelet coefficients at every level of DWT significantly impairs the performance of memory transaction on the GPU. To address these problems, we propose a mixed-band lifting wavelet transform that reduces uncoalesced global memory access on the GPU and maximizes on-chip memory bandwidth by implementing in-place operations using registers. We assess the performance of the proposed method by comparing with the state-of-the-art DWT libraries, and show its usability in a compressive sensing (CS) MRI application.
Keywords :
biomedical MRI; compressed sensing; data compression; discrete wavelet transforms; graphics processing units; image coding; medical image processing; multiprocessing systems; CS; DWT libraries; GPU; JPEG2000; compressive sensing MRI application; discrete wavelet transform; fast mixed-band lifting wavelet transform; graphics processing units; image compression applications; in-place operations; many-core accelerators; on-chip memory bandwidth; registers; uncoalesced global memory access; wavelet coefficients; Discrete wavelet transforms; Graphics processing units; Image reconstruction; Magnetic resonance imaging; Three-dimensional displays; CUDA; Compressive Sensing; Denoising; GPU; MRI; Mixed-band; Parallel Computing; Wavelet;
Conference_Titel :
Image Processing (ICIP), 2014 IEEE International Conference on
Conference_Location :
Paris
DOI :
10.1109/ICIP.2014.7025247