Abstract
In the report the optimization of image similarity metric computation method for three dimensional vector video with general-purpose computations on graphical processor unit (GPGPU) is discussed. The use of stream processors in graphics accelerators and Compute Unified Device Architecture (CUDA) platform allows significant performance gain in comparison to calculations on general-purpose processors, while solving problems of computer vision and image similarity determination. The performance of the GPGPU metric value computation is measured and researched.
Highlights
Video playback systems for three-dimensional vector format need to determine parameter types of shader programs contained in the video stream
The paper proposes to move its computation to graphics processing unit (GPU) in order to speed up the algorithm
Speeded Up Robust Features (SURF) method is used for point detection, the implementation of which is available for GPU [1, 2]
Summary
Video playback systems for three-dimensional vector format need to determine parameter types of shader programs contained in the video stream. This can be accomplished by creating profiles for each video source type. Profiling is resourceintensive task and the calculations cannot be performed in real time while running the application for which the profile is compiled. The longest stage of the method is the metric calculation. The paper proposes to move its computation to graphics processing unit (GPU) in order to speed up the algorithm
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