Performance of new GPU-based scan-conversion algorithm implemented using OpenGL.

Area of Science:

• Computer Graphics
• Image Processing
• GPU Computing

Background:

• Scan-conversion is a fundamental process in computer graphics for rasterizing geometric primitives.
• Traditional scan-conversion algorithms are often CPU-bound, limiting real-time performance.
• Evaluating new algorithms requires comparison against established methods in both speed and output quality.

Purpose of the Study:

• To introduce and evaluate a new GPU-based scan-conversion algorithm.
• To compare the computational performance of the GPU algorithm against common CPU-based methods.
• To assess and compare the image quality generated by the GPU algorithm versus traditional methods.

Main Methods:

• Implementation of a new scan-conversion algorithm utilizing graphics processing unit (GPU) acceleration with OpenGL.
• Performance benchmarking of the GPU algorithm against nearest-neighbor, linear, and bilinear interpolation algorithms executed on a central processing unit (CPU).
• Image quality assessment using signal-to-noise power ratio (SPR) as a quantitative metric.

Main Results:

• The GPU-based scan-conversion algorithm demonstrated significantly higher compute performance compared to the CPU-based algorithms.
• Image quality, measured by SPR, was found to be superior with the new GPU algorithm.
• The new algorithm effectively leverages GPU parallel processing capabilities for scan-conversion tasks.

Conclusions:

• The developed GPU-based scan-conversion algorithm presents a viable and efficient alternative to traditional CPU implementations.
• Significant improvements in both computational speed and image fidelity are achievable with GPU acceleration for scan-conversion.
• This approach holds promise for applications requiring high-performance, high-quality image generation in real-time graphics.