• Graininess corresponds to high frequency content in the picture
(II)
How do we measure graininess?
• If we low pass filter an image, then the graininess in the picture will become blurry
Monet
Monet after filtering
Rembrandt
Rembrandt after filtering
(III)
How exactly do we low pass filter?
• Apply a Wiener filter, which is designed to filter out constant power additive noise (MATLAB help documentation).
• The Wiener filter adaptively filters using statistics estimated from a local neighborhood of each pixel (MATLAB help documentation).
• The size of the neighborhood is set to be one-tenth of the size of the original picture.
(IV)
How do we measure blurriness?
• Blurriness corresponds to the amount of contrast in the picture.
High Contrast
Low Contrast
(V)
How do we measure contrast?
• Convert the image to gray scale in MATLAB.
• The image is then a matrix of values from zero to one where each value corresponds to the darkness of a pixel.
Example: Image of size 3x4 pixels
• Approximate the contrast by finding the sum of the differences between elements of a row and the sum of the differences of elements of a column.
Row contrast of example = 3.4
Column contrast of example = 2.7
• Add two sums for total contrast.
Total contrast of Example= Row contrast + Column Contrast = 6.1
(VI)
How do we compare the graininess of different pictures of different sizes?
• Measure the percent change in contrast to determine the blurriness.
(Contrast of Original – Contrast of Blurred)/ Contrast of Original
(VII)
Summary of Algorithm to Measure Graininess
(VIII)
Graininess Trends Found Among Three Artists
Graininess Trends: Approximated by Measuring Blurriness After Wiener Low Pass Filter
• Monet paintings found in percentage range of 76 to 92.
• Rembrandt paintings have no definite trend.
• Picasso paintings found in percentage range of 67 to 85.
• Results interesting but not useful for classification since ranges of artist overlap.
