Hi Muzammil,
It is my understanding that you're trying to clearly understand the differences between PSNR, SSIM, and SNR when comparing images or signals — especially how each one evaluates the similarity or quality of two images.
PSNR quantifies how much noise, or error exists between two images, typically an original and its compressed or degraded version and is based on the mean squared error between two images.
SSIM evaluates perceived image quality by comparing luminance, contrast, and Structural similarity. It is more suitable than PSNR for tasks like image compression evaluation, where perceptual quality matters.
SNR measures the ratio of the original signal power to the noise power. While PSNR is typically used for images and videos, SNR more often for signals like audio or EEG.
In summary, use PSNR when you're interested in objective, pixel-level differences (e.g., during algorithm development or tuning). You can use SSIM when you care about how the image looks to a human, especially for compression, denoising, or enhancement. You can use SNR mostly for 1D signals or in general-purpose signal-to-noise evaluations, not peak-based visual data. For more information, you can refer to the following documentations:
- psnr: https://www.mathworks.com/help/images/ref/psnr.html
- ssim: https://www.mathworks.com/help/images/ref/ssim.html
- snr: https://www.mathworks.com/help/signal/ref/snr.html
Hope this helps!
