METHODS OF DETECTING MOIRE ARTIFACTS

1 . A method of detecting Moire artefacts in a digital image, the method comprising:

calculating at least one parameter relating to at least one feature of the digital image;

comparing the at least one parameter to at least one corresponding threshold, wherein the at least one threshold is determined by a machine learning process, based on a set of example images and reference parameters for the set of example images; and

determining whether the digital image contains Moire artefacts based on the results of the comparing.

2 . The method as in claim 1 wherein the at least one feature is a measure of the luminance contrast between two or more adjacent pixels in the digital image.

3 . The method as in claim 1 wherein the at least one parameter is calculated using a histogram of luminance gradient orientations, HOG, between two or more adjacent pixels in the digital image.

4 . The method as in claim 1 wherein the at least one feature is a measure of the colour contrast between two or more adjacent pixels in the digital image.

5 . The method as in claim 1 wherein the at least one parameter is calculated using a histogram of luminance gradient orientations between adjacent pixels in the digital image and a histogram of colour channel gradient orientations between adjacent pixels in the digital image.

6 . The method as in claim 1 wherein the feature is the mean grey level.

7 . The method as in claim 1 wherein the at least one parameter is calculated in a subset of pixels of the digital image.

8 . A method of determining the quality of a printed image, the method comprising:

scanning the printed image to create a scanned digital image;

determining the location of artefacts in the scanned digital image by comparing the scanned digital image to an original digital copy of the printed image;

using a classifier generated by a machine learning process to classify which of the artefacts, if any, in the scanned digital image are Moire artefacts; and

determining the quality of the printed image based on those artefacts that were not classed as Moire artefacts.

9 . (canceled)

10 . The method as in claim 9 wherein using the classifying module comprises calculating at least one parameter for the digital image; and

wherein the classifying module compares the at least one parameter of the digital image to the values of the at least one parameter calculated for the example images.

11 . A classifier for determining if a digital image contains Moire artefacts, the classifier comprising:

at least one threshold relating to at least one feature of the digital image, wherein the at least one threshold was determined by a machine learning process, based on a set of example images and reference parameters for the set of example images.

12 . The classifier as in claim 10 wherein the classifier comprises a decision tree.

13 . The classifier as in claim 10 wherein the at least one threshold defines a hyperplane or line in a feature space that separates images with Moire artefacts from images with no Moire artefacts.

14 . The classifier as in claim 10 wherein the at least one feature is a measure of the luminance contrast between two or more adjacent pixels in the digital image.

15 . The classifier as in claim 10 wherein the at least one feature is a measure of the colour contrast between two or more adjacent pixels in the digital image.

16 . The classifier as in claim 10 wherein the feature is the mean grey level.