PSNR اور SSIM امیج کوالٹی میٹرکس

Peak signal-to-noise ratio, or PSNR, expresses mean squared pixel error on a logarithmic decibel scale relative to the maximum possible pixel value. Higher PSNR means lower squared error under the specified representation. It helps compare aligned images with consistent ranges. However, it treats pixel differences uniformly and does not model all perceptual effects. A small shift in a sharp edge can create substantial pixel error even if the image looks similar, while a smooth result can score well despite losing texture that matters to a viewer or task. Structural similarity, commonly SSIM, compares local patterns through luminance, contrast, and structure terms. Its design reflects the idea that perceived similarity depends on more than raw per-pixel error. SSIM can align better with some visual judgments in some settings, but it is not a universal perceptual oracle. Its score depends on implementation choices such as windowing, scale, color handling, and aggregation. Distortions that matter for a particular application may not be well captured. Metric values only make sense with a defined evaluation protocol. Images should be spatially aligned, and the peak or data range supplied to PSNR must match the pixel representation. Comparing 8-bit values with normalized floats using the wrong range produces invalid results. Cropping borders, converting color spaces, and handling alpha channels can also change scores. Report these choices alongside results. PSNR and SSIM can disagree because one measures squared pixel error while the other examines local structure. Neither tells whether a reconstructed image preserves a small tumor boundary, a character in a document, or a feature used by an automated system. For medical, scientific, or machine-learning applications, choose additional task-specific measurements and inspect failures. Use a representative test set and avoid tuning repeatedly against it. Show distributions or per-image values rather than only a mean when outliers matter. A metric is evidence about a defined property, not a complete substitute for human review or task validation.