1. Histograms – Chapter 4
Continued

2. 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

3. Effects of JPEG compression
Original, uncompressed file
229K bytes
Smooth histogram

4. Effects of JPEG compression
JPEG compressed file
33K bytes
Smooth histogram – not much change, image visually lossless

5. Effects of JPEG compression
JPEG compressed file
18K bytes
Histogram showing spikes – image starting to blur

6. Effects of JPEG compression
JPEG compressed file
12K bytes
Histogram showing spikes – image more blurring

7. Effects of JPEG compression
JPEG compressed file
6K bytes
Histogram showing tall spikes – image blocking, color fidelity lost

8. Effects of JPEG compression
JPEG compressed file
5K bytes
Histogram showing tall spikes – image blocking, color fidelity lost

9. Effects of JPEG compression
JPEG compressed file
3K bytes
Histogram showing tall spikes – image wrecked

10. Effects of JPEG compression
JPEG compressed file
3K bytes
Histogram showing tall spikes and gaps – image wrecked

11. Effects of JPEG compression
JPEG compressed file
2K bytes
Histogram showing tall spikes and gaps – image, what snake?

12. 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

13. 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

14. 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

15. Effects of saturation Original, looks decent enough
Histogram is smooth, contrast is a little low, exposure a little low, dynamic range a little low

16. 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

17. 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)

18. 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]]; }

19. 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

20. 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 = 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

21. Color histograms
Luminance
Red
Green
Blue

22. 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

23. Color histograms
Another option is to create a three dimensional histogram or scatter plot of the image pixel colors

24. Cumulative histogram
A bin in the cumulative histogram is the sum of all lower bins of the “normal” histogram
It’s not overly useful for analysis but will provide some needed information for certain image enhancement operations

25. Cumulative histogram

26. Cumulative histogram
I’ve placed a plugin for the cumulative histogram on the website
Download it and drop it into the folder
It will show up under the Plugins->Chapter04 menu
C:\Program Files\ImageJ\plugins\Chapter04