PSNR Peak Signal to Noise Ratio

PSNR, or Peak Signal-to-Noise Ratio, is a widely used metric in image and video processing to measure the quality of a reconstructed or compressed image/video in comparison to the original, uncompressed version. It provides an objective assessment of the fidelity or accuracy of the reconstructed signal by quantifying the amount of noise or distortion introduced during the compression or reconstruction process.

To understand PSNR, let's break down the components of the term. "Peak Signal" refers to the maximum possible intensity value that can be represented in the image or video, usually determined by the bit depth of the pixel values. For example, in an 8-bit grayscale image, the peak signal value would be 255 (2^8 - 1). In a color image with RGB channels, each channel may have a peak signal value of 255 as well.

"Noise Ratio" represents the difference or distortion between the original signal and the reconstructed or compressed signal. It quantifies the amount of error or loss that has occurred during compression or reconstruction. The difference is typically measured using Mean Squared Error (MSE), which calculates the average of the squared differences between corresponding pixels in the original and reconstructed images.

The formula for PSNR is as follows:

PSNR = 10 * log10((Peak Signal^2) / MSE)

The result is expressed in decibels (dB), which is a logarithmic scale commonly used to measure the ratio between two signal powers. A higher PSNR value indicates a higher quality reconstruction, as it means the reconstructed signal has less noise or distortion compared to the original signal.

PSNR is a popular metric because it is intuitive, easy to calculate, and provides a single value that summarizes the quality of the reconstruction. However, it has certain limitations. PSNR does not always correlate well with perceived image quality by humans, especially for high-quality images or videos where small differences may not be noticeable. Additionally, PSNR assumes that all image or video regions are equally important, which may not be true in some applications where specific regions require more attention.

Despite its limitations, PSNR remains a widely used metric in the field of image and video processing, particularly in the evaluation and comparison of compression algorithms, as it provides a standardized and quantitative measure of the reconstruction quality.