Calculating Image Peak Signal-to-Noise Ratio (PSNR)

You can implement a dedicated function for calculating Peak Signal-to-Noise Ratio (PSNR) in your codebase. This utility function proves extremely valuable for quantitative image quality assessment. PSNR serves as a widely-adopted metric that quantifies the difference between original images and their compressed or distorted versions. The implementation typically involves calculating the Mean Square Error (MSE) between two images first, then deriving PSNR using the formula: PSNR = 20·log10(MAX_I/√MSE), where MAX_I represents the maximum possible pixel value (255 for 8-bit images). Key implementation considerations include handling different image formats (grayscale vs. color), managing potential division-by-zero cases when images are identical, and ensuring proper data type conversions. For color images, you might compute PSNR for each channel separately or convert to luminance space first. The function should accept two image matrices as input and return the PSNR value in decibels (dB). Integrating this PSNR calculation function enables objective evaluation of image distortion levels, facilitating optimization and refinement of image processing algorithms. This enhancement significantly improves your code's completeness and practical utility for image analysis tasks.