Accelerating Wavelet Lifting on Graphics Hardware Using CUDA

The Discrete Wavelet Transform (DWT) has a wide range of applications from signal processing to video and image compression. We show that this transform, by means of the lifting scheme, can be performed in a memory and computation-efficient way on modern, programmable GPUs, which can be regarded as massively parallel coprocessors through NVidia’s CUDA compute paradigm. The three main hardware architectures for the 2D DWT (row-column, line-based, block-based) are shown to be unsuitable for a CUDA implementation. Our CUDA-specific design can be regarded as a hybrid method between the row-column and block-based methods. We achieve considerable speedups compared to an optimized CPU implementation and earlier non-CUDA-based GPU DWT methods, both for 2D images and 3D volume data. Additionally, memory usage can be reduced significantly compared to previous GPU DWT methods. The method is scalable and the fastest GPU implementation among the methods considered. A performance analysis shows that the results of our CUDA-specific design are in close agreement with our theoretical complexity analysis.

Fast Sparse Level Sets on Graphics Hardware

<p>The level-set method is one of the most popular techniques for capturing and tracking deformable interfaces. Although level sets have demonstrated great potential in visualization and computer graphics applications, such as surface editing and physically based modeling, their use for interactive simulations has been limited due to the high computational demands involved. In this paper, we address this computational challenge by leveraging the increased computing power of graphics processors, to achieve fast simulations based on level sets. Our efficient, sparse GPU level-set method is substantially faster than other state-of-the-art, parallel approaches on both CPU and GPU hardware. We further investigate its performance through a method for surface reconstruction, based on GPU level sets. Our novel multiresolution method for surface reconstruction from unorganized point clouds compares favorably with recent, existing techniques and other parallel implementations. Finally, we point out that both level-set computations and rendering of level-set surfaces can be performed at interactive rates, even on large volumetric grids. Therefore, many applications based on level sets can benefit from our sparse level-set method.</p>

Accelerating Wavelet-Based Video Coding on Graphics Hardware using CUDA

The Discrete Wavelet Transform (DWT) has a wide range of applications from signal processing to video and image compression. This transform, by means of the lifting scheme, can be performed in a memory and computation efficient way on modern, programmable GPUs, which can be regarded as massively parallel co-processors through NVidia’s CUDA compute paradigm. The method is scalable and the fastest GPU implementation among the methods considered. We have integrated our DWT into the Dirac Wavelet Video Codec (DWVC), of which the overlapped block motion compensation compensation and frame arithmetic have been accelerated using CUDA as well.

Accelerating Wavelet-Based Video Coding on Graphics Hardware using CUDA

The Discrete Wavelet Transform (DWT) has a wide range of applications from signal processing to video and image compression. This transform, by means of the lifting scheme, can be performed in a memory mid computation efficient way on modern, programmable GPUs, which can be regarded as massively parallel co-processors through NVidia's CUDA compute paradigm. The method is scalable and the fastest GP U implementation among the methods considered. We have integrated our DWT into the Dirac Wavelet Video Codec (DWVC), of which the overlapped block motion compensation compensation and frame arithmetic have been accelerated using CUDA as well

A Memory and Computation Efficient Sparse Level-Set Method

Since its introduction, the level set method has become the favorite technique for capturing and tracking moving interfaces, and found applications in a wide variety of scientific fields. In this paper we present efficient data structures and algorithms for tracking dynamic interfaces through the level set method. Several approaches which address both computational and memory requirements have been very recently introduced. We show that our method is up to 8.5 times faster than these recent approaches. More importantly, our algorithm can greatly benefit from both fine- and coarse-grain parallelization by leveraging SIMD and/or multi-core parallel architectures.