Mobile Computational Photography with FCam

Pulli, Kari; Troccoli, Alejandro

doi:10.1007/978-3-642-44907-9_11

Cited by 2 publications

(1 citation statement)

References 12 publications

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“…Tegra is developed for applications such as rendering (Mobeen & Lin, 2012;Rodríguez & Alcocer, 2012;Q. Wang, Yu, Rasmussen, & Yu, 2014), ray tracing (Lee et al, 2013), optical flow (Plyer, Le Besnerais, & Champagnat, 2014), face recognition (Kwang-Ting & Yi-Chu, 2011;Y.-C. Wang, Donyanavard, & Cheng, 2012), object tracking (Růžička & Mašek, 2014), computational photography (Pulli & Troccoli, 2014) and sift detector (Rister, Guohui, Wu, & Cavallaro, 2013).…”

Section: Introductionmentioning

confidence: 99%

Mobile GPU Computing Based Filter Bank Convolution for Three-Dimensional Wavelet Transform

Zhao

2016

International Journal of Mobile Computing and Multimedia Communications

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Mobile GPU computing, or System on Chip with embedded GPU (SoC GPU), becomes in great demand recently. Since these SoCs are designed for mobile devices with real-time applications such as image processing and video processing, high-efficient implementations of wavelet transform are essential for these chips. In this paper, the author develops two SoC GPU based DWT: signal based parallelization for discrete wavelet transform (sDWT) and coefficient based parallelization for discrete wavelet transform (cDWT), and the author evaluates the performance of three-dimensional wavelet transform on SoC GPU Tegra K1. Computational results show that, SoC GPU based DWT is significantly faster than SoC CPU based DWT. Computational results also show that, sDWT can generally satisfy the requirement of real-time processing (30 frames per second) with the image sizes of 352×288, 480×320, 720×480 and 1280×720, while cDWT can only obtain read-time processing with small image sizes of 352×288 and 480×320.

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