Difference between PSNR and SSIM comparison

Hello everyone.

I have been reading about this but couldn't understand properly that's why posting it here. How would we differentiate between PSNR and SSIM comparison of two images? I mean when we compare two images, what are we really looking into via PSNR comparison and what are we looking into via SSIM comparison? Also, how is SNR different from PSNR? Kindly explain in the context of images or 1D signals.

Thanks in advance.

Answer 1

[Prasanna]

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!

[Image Analyst]

Also you might look at niqe and brisque

help niqe

 niqe - Naturalness Image Quality Evaluator (NIQE) no-reference image quality score
    This MATLAB function calculates the no-reference image quality score for
    image A using the Naturalness Image Quality Evaluator (NIQE).

    Syntax
      score = niqe(A)
      score = niqe(A,model)

    Input Arguments
      A - Input image
        2-D grayscale image | 2-D RGB image
      model - Custom model
        niqeModel object

    Output Arguments
      score - No-reference image quality score
        nonnegative scalar

    Examples
      openExample('images/CalculateNIQEScoreUsingDefaultFeatureModelExample')
      openExample('images/CalculateNIQEScoreUsingCustomFeatureModelExample')

    See also brisque, fitbrisque, fitniqe, piqe, niqeModel

    Introduced in Image Processing Toolbox in R2017b
    Documentation for niqe
       doc niqe

help brisque

 brisque - Blind/Referenceless Image Spatial Quality Evaluator (BRISQUE) no-reference image quality score
    This MATLAB function calculates the no-reference image quality score for
    image I using the Blind/Referenceless Image Spatial Quality Evaluator
    (BRISQUE).

    Syntax
      score = brisque(I)
      score = brisque(I,model)

    Input Arguments
      I - Input image
        2-D grayscale image | 2-D RGB image
      model - Custom model
        brisqueModel object

    Output Arguments
      score - No-reference image quality score
        nonnegative scalar

    Examples
      openExample('images/CalculateBRISQUEScoreUsingDefaultFeatureModelExample')
      openExample('images/CalculateBRISQUEScoreUsingCustomFeatureModelExample')

    See also fitbrisque, niqe, fitniqe, piqe, brisqueModel

    Introduced in Image Processing Toolbox in R2017b
    Documentation for brisque
       doc brisque