Object oriented framework for real-time image processing on GPU

Seiller, Nicolas; Singhal, Nitin; Park, In Kyu

doi:10.1109/icip.2010.5651682

Cited by 6 publications

(3 citation statements)

References 10 publications

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“…The above progress can be represented by formula (5). We fill zero in the last column and the last row of the original block so that the integral graph can be well obtained without data movement.…”

Section: The Sad/satd Optimization Algorithmmentioning

confidence: 99%

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A Novel Parallel Motion Estimation Design and Implementation on GPU

Zhang

Zhao

et al. 2019

IEEE Access

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The development of high-resolution video mounts a serious challenge to the previous video coding standard. The appearance of the new generation standards greatly relieves the dilemma but increases the coding complexity dramatically. Motion estimation is considered as the module with a relatively high computational complexity. In this paper, a parallel motion estimation implementation is proposed, which includes pre-motion estimation, integer motion estimation, and fractional motion estimation. They are highly accelerated on GPU based on AVS2, which is one of the new generation standards. A rapid mapping table algorithm is introduced to improve the efficiency of data access. In addition, a quasi-integral-graph algorithm is designed to calculate SAD or SATD efficiently for blocks of different sizes. The two novel techniques can effectively improve the utilization and efficiency of threads and exploit the characteristics of GPU. The experimental results show that the proposed parallel method can effectively accelerate the motion estimation.

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Section: The Sad/satd Optimization Algorithmmentioning

confidence: 99%

“…GPU is characterized by the architecture of multi-core processors and multi-threads computing. It has been widely used in many fields such as image processing [5], [6], deep learning [7].…”

Section: Introductionmentioning

confidence: 99%

A Novel Parallel Motion Estimation Design and Implementation on GPU

Zhang

Zhao

et al. 2019

IEEE Access

View full text Add to dashboard Cite

show abstract

“…and Tesla series, AMD's Northern Island series, and ATI Radeon R300 series [4][5][6]. Since restoring color in grayscale images enhances the visual cognition of image data, employing the proposed parallelized high-performance approach for colorizing grayscale images can thereby be greatly beneficial while working with huge image datasets, such as those obtained from satellite imagery, surveillance imaging data, and medical imaging data, wherein a single image can in be the size-range of gigabytes.…”

Section: Introductionmentioning

confidence: 99%