brisque

Compute the BRISQUE score for a natural image and its distorted versions using the default model.

Read an image into the workspace. Create copies of the image with noise and blurring distortions.

I = imread("lighthouse.png");
Inoise = imnoise(I,"salt & pepper",0.02);
Iblur = imgaussfilt(I,2);

Display the images.

montage({I,Inoise,Iblur},Size=[1 3],ThumbnailSize=[])
title("Original Image | Noisy Image | Blurry Image")

Calculate the BRISQUE score for each image using the default model, and display the score.

brisqueI = brisque(I);
disp("BRISQUE score for the original image is: "+brisqueI)

BRISQUE score for the original image is: 20.6586

brisqueInoise = brisque(Inoise);
disp("BRISQUE score for the noisy image is: "+brisqueInoise)

BRISQUE score for the noisy image is: 52.6074

brisqueIblur = brisque(Iblur);
disp("BRISQUE score for the blurry image is: "+brisqueIblur)

BRISQUE score for the blurry image is: 47.7553

The original undistorted image has the best perceptual quality and therefore the lowest BRISQUE score.

Train a custom BRISQUE model from a set of quality-aware features and corresponding human opinion scores. Use the custom model to calculate a BRISQUE score for an image of a natural scene.

Save images from an image datastore. These images all have compression artifacts resulting from JPEG compression.

setDir = fullfile(toolboxdir('images'),'imdata');
imds = imageDatastore(setDir,'FileExtensions',{'.jpg'});

Specify the opinion score for each image. The following differential mean opinion score (DMOS) values are for illustrative purposes only. They are not real DMOS values obtained through experimentation.

opinionScores = 100*rand(1,size(imds.Files,1));

Create the custom model of quality-aware features using the image datastore and the opinion scores. Because the scores are random, the property values will vary.

model = fitbrisque(imds,opinionScores')

Extracting features from 33 images.
......Training support vector regressor...

Done.

model = 
  brisqueModel with properties:

             Alpha: [31x1 double]
              Bias: 58.0900
    SupportVectors: [31x36 double]
            Kernel: 'gaussian'
             Scale: 0.3729

Read an image of a natural scene that has the same type of distortion as the training images. Display the image.

I = imread('car1.jpg');
imshow(I)

Calculate the BRISQUE score for the image using the custom model. Display the score.

brisqueI = brisque(I,model);
fprintf('BRISQUE score for the image is %0.4f.\n',brisqueI)

BRISQUE score for the image is 79.8961.