Peak Signal-to-Noise Ratio (PSNR) Calculator for Image Comparison

Peak Signal-to-Noise Ratio (PSNR) is a widely used metric for evaluating image reconstruction quality, particularly prevalent in applications such as image compression, denoising, and super-resolution reconstruction. PSNR assesses reconstruction quality by comparing differences between original and processed images, where higher values indicate less distortion and better quality. In code implementation, this typically involves processing images as matrices and performing element-wise comparisons.

PSNR calculation is based on Mean Squared Error (MSE), which represents the average of squared differences between pixel values of two images. The computational process first calculates MSE by summing squared differences across all pixels and dividing by total pixel count. Then, using the maximum possible pixel value (e.g., 255 for 8-bit images) as the peak signal reference, the logarithmic ratio is computed. PSNR is measured in decibels (dB) - identical images yield infinite PSNR, while significantly different images show substantially lower values. Key functions in implementation would include matrix subtraction, squaring operations, and logarithmic transformations.

In practical applications, while PSNR is computationally straightforward, it doesn't fully correlate with human visual perception of image quality. Therefore, it's often used alongside other metrics like Structural Similarity Index (SSIM). Despite this limitation, PSNR remains an important benchmark in image processing due to its computational efficiency and intuitive interpretation. Code implementations typically include normalization steps and handling of different image bit-depths through maximum value parameters.