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Naturalness Image Quality Evaluator (NIQE) no-reference image quality score



score = niqe(A) calculates the no-reference image quality score for image A using the Naturalness Image Quality Evaluator (NIQE). niqe compares A to a default model computed from images of natural scenes. A smaller score indicates better perceptual quality.


score = niqe(A,model) calculates the image quality score using a custom model.


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Compute the NIQE 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])
title('Original Image | Noisy Image | Blurry Image')

Calculate the NIQE score for each image using the default model. Display the score.

niqeI = niqe(I);
fprintf('NIQE score for original image is %0.4f.\n',niqeI)
NIQE score for original image is 2.5455.
niqeInoise = niqe(Inoise);
fprintf('NIQE score for noisy image is %0.4f.\n',niqeInoise)
NIQE score for noisy image is 10.8770.
niqeIblur = niqe(Iblur);
fprintf('NIQE score for blurry image is %0.4f.\n',niqeIblur)
NIQE score for blurry image is 5.2661.

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

Load a set of natural images into an image datastore. These images are shipped in Image Processing Toolbox™ in a directory named 'imdata'.

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

Train a custom NIQE model using the image datastore.

model = fitniqe(imds);
Extracting features from 33 images.
Completed 14 of 33 images.  Time: Calculating...

Read an image of a natural scene. Display the image.

I = imread('car1.jpg');

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

niqeI = niqe(I,model);
fprintf('NIQE score for the image is %0.4f.\n',niqeI)
NIQE score for the image is 2.6700.

Input Arguments

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Input image, specified as a 2-D grayscale or RGB image.

Data Types: single | double | int16 | uint8 | uint16

Custom model of image features, specified as a niqeModel object. model is derived from natural scene statistics.

Output Arguments

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No-reference image quality score, returned as a nonnegative scalar. Lower values of score reflect better perceptual quality of image A with respect to the input model.

Data Types: double


NIQE measures the distance between the NSS-based features calculated from image A to the features obtained from an image database used to train the model. The features are modeled as multidimensional Gaussian distributions.


[1] Mittal, A., R. Soundararajan, and A. C. Bovik. "Making a Completely Blind Image Quality Analyzer." IEEE Signal Processing Letters. Vol. 22, Number 3, March 2013, pp. 209–212.

Version History

Introduced in R2017b

See Also