Computation of gray-level co-occurrence matrix based on CUDA and its optimization

TL;DR

This paper presents an optimized CUDA-based parallel algorithm for computing gray-level co-occurrence matrices (GLCM), achieving up to 50 times faster performance than CPU implementations without sacrificing accuracy.

Contribution

It introduces a novel GPU-accelerated method with specific strategies like image partitioning to significantly enhance GLCM computation speed.

Findings

GPU-based GLCM computation is 50 times faster than CPU.

Optimization strategies improve parallel processing efficiency.

Accuracy of GLCM is maintained despite speed improvements.

Abstract

As in various fields like scientific research and industrial application, the computation time optimization is becoming a task that is of increasing importance because of its highly parallel architecture. The graphics processing unit is regarded as a powerful engine for application programs that demand fairly high computation capabilities. Based on this, an algorithm was introduced in this paper to optimize the method used to compute the gray-level co-occurrence matrix (GLCM) of an image, and strategies (e.g., "copying", "image partitioning", etc.) were proposed to optimize the parallel algorithm. Results indicate that without losing the computational accuracy, the speed-up ratio of the GLCM computation of images with different resolutions by GPU by the use of CUDA was 50 times faster than that of the GLCM computation by CPU, which manifested significantly improved performance.