The Performance of Histogram Processing in Real Time

This research we evaluate the performance of histogram processing, i.e. histogram equalization, CLAHE and histogram matching both for static image and real time frame captured by a camera. For static image, we evaluate objective measures i.e. intensity mean, entropy, PSNR and SSIM. For real time frame, we evaluate the average execution time. The experiment result shows the CLAHE image is closer to the original in terms of pixel values (higher PSNR) and higher similarity between the images in terms of structure, contrast, and luminance (higher SSIM) and has a shorter execution time. The experiment is conducted using programming language python and openCV library.