Histograms – Chapter 4
Continued
ImageJ – from last time…
• Open snake.png (download from my web site)
• Select Analyze/Histogram
– This is the histogram of the luminance channel of the color
image
• Select Image/Color/Split Channels
– You now have the red/green/blue channels individually
• Create histograms of each of these
• Comment on exposure, contrast, dynamic range
• Pull other images from wherever, play with it
Effects of JPEG compression
• Original, uncompressed file
• 229K bytes
• Smooth histogram
Effects of JPEG compression
• JPEG compressed file
• 33K bytes
• Smooth histogram – not much change, image visually lossless
Effects of JPEG compression
• JPEG compressed file
• 18K bytes
• Histogram showing spikes – image starting to blur
Effects of JPEG compression
• JPEG compressed file
• 12K bytes
• Histogram showing spikes – image more blurring
Effects of JPEG compression
• JPEG compressed file
• 6K bytes
• Histogram showing tall spikes – image blocking, color fidelity
lost
Effects of JPEG compression
• JPEG compressed file
• 5K bytes
• Histogram showing tall spikes – image blocking, color fidelity
lost
Effects of JPEG compression
• JPEG compressed file
• 3K bytes
• Histogram showing tall spikes – image wrecked
Effects of JPEG compression
• JPEG compressed file
• 3K bytes
• Histogram showing tall spikes and gaps – image wrecked
Effects of JPEG compression
• JPEG compressed file
• 2K bytes
• Histogram showing tall spikes and gaps – image, what snake?
ImageJ JPEG
• Edit->Options->Input/Output…
– Allows you to set level of compression
– Smaller number gives more compression
at the cost of greater information loss
– Larger number retains more information at
the cost of less compression
Effects of saturation
• Ideally the sensor’s range (detectable
light levels from dark to bright) is greater
than that of the scene being imaged
• Realistically, this doesn’t always happen
• The human visual system has incredible
dynamic range due to the distribution of
rods and cones in the retina
• Very difficult to match in silicon
Human visual system
• Cones: process color (3 types)
• Primarily in the foveal region of the retina
• Rods: process shades of gray
• Primarily in the peripheral region of the retina
Effects of saturation
• Original, looks decent enough
• Histogram is smooth, contrast is a little low, exposure a little
low, dynamic range a little low
Effects of saturation
•
•
We brighten it up by multiplying pixels by some specified values
(artificially brighten it to compensate for the sensor deficiencies)
Histogram shows dynamic range is good, contrast is good, but we get
spikes and a lot of saturated pixels
Playing detective
• When you see stuff like this in the histogram, you
know something is awry
• If you see spikes, either the
imaging device was bad or the
image was digitally manipulated
• Analog pixels don’t do this
• Ditto if you see gaps
• If you see a lot of saturation
either the photographer screwed
up or the image was digitally
manipulated (or the scene was
too bright for the camera)
Computing histograms
• Because you should at least see the code…
int Histogram[] = new int[256]; // 8 bit histogram
for (int i = 0; i < imageHeight; i++) {
for (int j = 0; j < imageWidth; j++) {
++Histogram[image[i][j]];
}
}
Computing histograms
• There may be times when your histogram is bigger
than your memory size
– Large bit-depth images
– Floating point images
• All is not lost, we just resort to binning
– In this case a single histogram entry represents a range of
pixel values [rather than a single value]
– Be careful when reading these as spikes/gaps could be
hidden in the representation
Computing histograms
• Histograms of color images create another situation
to be dealt with
• Color images are typically 24-bits
– 8-bits each for Red, Green, and Blue
– Results in 224 = 16777216 pixel values
– Not suitable for direct histogram creation and binning makes
not sense – why?
• There is not natural ordering of the colors as there is in a gray scale
image
– Two choices
• Display the luminance histogram (the brightness content of the image)
• Display the red, green, and blue histograms separately
Color histograms
Luminance
Red
Green
Blue
Color histograms
• The green histogram will [typically] look similar to the
luminance histogram
• If the red, green, and blue histograms look similar the
image has most likely been white balanced
– Two of the channels (e.g. red, and blue) have been
enhanced (contrast, dynamic range) to look similar to the
third (e.g. green)
– This “trick” is performed to make sure that white objects in
the scene appear white in the resulting image
– Corrects for deficiencies in the imaging device
Color histograms
• Another option is to create a three dimensional histogram or
scatter plot of the image pixel colors
Cumulative histogram
• A bin in the cumulative histogram is the sum of all
lower bins of the “normal” histogram
i
cum ulativeH (i)   histogram( j )
0i K
j 0
• It’s not overly useful for analysis but will provide
some needed information for certain image
enhancement operations
Cumulative histogram
Cumulative histogram
• I’ve placed a plugin for the cumulative
histogram on the website
• Download it and drop it into the folder
C:\Program Files\ImageJ\plugins\Chapter04
• It will show up under the Plugins->Chapter04
menu