• 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.