Histograms – Chapter 4

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 = 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

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