Abstract
In this paper a parallel image processing and frame rate stabilization approach is proposed. This approach works on a regular PC with a multi-core CPU. It is implemented under .NET Framework and tested on Microsoft Windows 7 operating system, performing several experiments. It is also applied to a face recognition application to increase its image processing performance successfully. Results show that, handled workload when 4 physical cores are used is approximately 5.25 times the workload handled with one core. It is also shown that the approach successfully distributes the workload on CPU cores and produces output at a stable frame rate under both steady and unsteady workloads. This approach can be used for various signal processing or multimedia applications to parallelize their tasks to increase the performance on multi-core CPUs. DOI: http://dx.doi.org/10.5755/j01.eie.23.6.19696
Highlights
High demand for multimedia content has increased enormously for the past decade with the excessive use of social media
A real-time image processing and frame rate stabilization approach, which works on regular multi-core CPUs and does not use an explicit hardware such as graphics processing units (GPUs) or Compute Unified Device Architecture (CUDA), was proposed
Results were compared to single core results
Summary
High demand for multimedia content has increased enormously for the past decade with the excessive use of social media. Real-time image and/or video processing are computationally intensive. In order to address this challenge, graphics processing units (GPUs), which are processors, specialized for processing graphics, and technologies like NVIDIA's Compute Unified Device Architecture (CUDA) have become popular among image processing community. GPUs are specialized for graphics rendering, but their processing capabilities have been used for non-graphics applications. GPUs have cores that can process parallel workloads. There are many studies on methods to accelerate or implement various algorithms in many fields [1]–[7], especially in image processing [8]–[12], mostly using GPUs. Most of the existing approaches have focused on parallelizing a specific algorithm by partitioning it into parallelizable steps [13]–[16]. Images have been partitioned into blocks and filters have been applied on these blocks with some more operations, to make
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