GPU-based 3D lower tree wavelet video encoder

Galiano, Vicente; López-Granado, Otoniel; Malumbres, Manuel P.; Drummond, L. A.; Migallón, Héctor

doi:10.1186/1687-6180-2013-24

Cited by 4 publications

(4 citation statements)

References 16 publications

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“…A number of the implementations for discrete wavelet transform for both shared memory computers and GPU have been developed and discussed [30,31,33,38,39,40]. The parallel implementation of non-adaptive wavelet transform for parallel computers with distributed architectures have been developed as well, e.g., [26,27,28,29].…”

Section: Asynchronous Parallel Second-generation Wavelet Transformmentioning

confidence: 99%

Parallel adaptive wavelet collocation method for PDEs

Nejadmalayeri¹,

Vezolainen

Brown-Dymkoski

et al. 2015

Journal of Computational Physics

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Please cite this article in press as: A. Nejadmalayeri et al., Parallel adaptive wavelet collocation method for PDEs, J. Comput. Phys. (2015), http://dx. AbstractA parallel adaptive wavelet collocation method for solving a large class of Partial Differential Equations is presented. The parallelization is achieved by developing an asynchronous parallel wavelet transform, which allows one to perform parallel wavelet transform and derivative calculations with only one data synchronization at the highest level of resolution. The data are stored using tree-like structure with tree roots starting at a priori defined level of resolution. Both static and dynamic domain partitioning approaches are developed. For the dynamic domain partitioning, trees are considered to be the minimum quanta of data to be migrated between the processes. This allows fully automated and efficient handling of non-simply connected partitioning of a computational domain. Dynamic load balancing is achieved via domain repartitioning during the grid adaptation step and reassigning trees to the appropriate processes to ensure approximately the same number of grid points on each process. The parallel efficiency of the approach is discussed based on parallel adaptive wavelet-based Coherent Vortex Simulations of homogenous turbulence with linear forcing at effective non-adaptive resolutions up to 2048 3 using as many as 2048 CPU cores.

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Section: Asynchronous Parallel Second-generation Wavelet Transformmentioning

confidence: 99%

Parallel adaptive wavelet collocation method for PDEs

Nejadmalayeri¹,

Vezolainen

Brown-Dymkoski

et al. 2015

Journal of Computational Physics

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“…DWT has an advantage easy to use and minimise the computation time and can be used for a finite group of data. It also, provides flexible multi-resolution analysis of an image [29,30]. DWT is used in signal and image processing in order to obtain the useful information from the signal or image that can be used for further analysis.…”

Section: Discrete Wavelet Transforms (Dwt)mentioning

confidence: 99%

Cutting tool tracking and recognition based on infrared and visual imaging systems using principal component analysis (PCA) and discrete wavelet transform (DWT) combined with neural networks

Elgargni

Al‐Habaibeh

Lotfi

2014

Int J Adv Manuf Technol

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The implementation of computerised condition monitoring systems for the detection cutting tools' correct installation and fault diagnosis is of a high importance in modern manufacturing industries. The primary function of a condition monitoring system is to check the existence of the tool before starting any machining process and ensure its health during operation. The aim of this study is to assess the detection of the existence of the tool in the spindle and its health (i.e. normal or broken) using infrared and vision systems as a noncontact methodology. The application of Principal Component Analysis (PCA) and Discrete Wavelet Transform (DWT) combined with neural networks are investigated using both types of data in order to establish an effective and reliable novel software program for tool tracking and health recognition. Infrared and visual cameras are used to locate and track the cutting tool during the machining process using a suitable analysis and image processing algorithms. The capabilities of PCA and Discrete Wavelet Transform (DWT) combined with neural networks are investigated in recognising the tool's condition by comparing the characteristics of the tool to those of known conditions in the training set. The experimental results have shown high performance when using the infrared data in comparison to visual images for the selected image and signal processing algorithms.

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“…The results show that in both performance dimensions, our enhanced encoder achieves good results compared with Kakadu and SPIHT encoders (optimized versions of JPEG2000), achieving speedups of six times. In the article entitled 'GPU-based 3D lower tree wavelet video encoder' [11] by Vicente Galiano et al, the authors introduce a fast GPU-based encoder which uses 3D-discrete wavelet transform and lower trees, presenting also an exhaustive analysis of the use of GPU memory. This approach demonstrates a good trade-off between R/D, coding delay (as fast as MPEG-2 for high definition) and memory requirements (up to six times less memory than ×264 encoder).…”

Section: Specific Contributionsmentioning

confidence: 99%

Hardware implementation of machine vision systems: image and video processing

Botella

Garcı́a

Meyer-Bäse

2013

EURASIP J. Adv. Signal Process.

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This contribution focuses on different topics covered by the special issue titled 'Hardware Implementation of Machine vision Systems' including FPGAs, GPUS, embedded systems, multicore implementations for image analysis such as edge detection, segmentation, pattern recognition and object recognition/interpretation, image enhancement/restoration, image/video compression, image similarity and retrieval, satellite image processing, medical image processing, motion estimation, neuromorphic and bioinspired vision systems, video processing, image formation and physics based vision, 3D processing/coding, scene understanding, and multimedia.

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GPU-based 3D lower tree wavelet video encoder

Cited by 4 publications

References 16 publications

Parallel adaptive wavelet collocation method for PDEs

Parallel adaptive wavelet collocation method for PDEs

Cutting tool tracking and recognition based on infrared and visual imaging systems using principal component analysis (PCA) and discrete wavelet transform (DWT) combined with neural networks

Hardware implementation of machine vision systems: image and video processing

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