1. Histograms – Chapter 4

2. Huh? That image is too contrasty. The colors aren’t vibrant enough. I want the reds to pop. It doesn’t have a warm enough feel. etc. etc. etc. The industry is rife with such statements that no one really knows how to interpret consistently

3. Some examples

5. The goal We know when a picture “looks” good We know when a picture “looks” bad –But this is purely subjective Sometimes we know what the reality is –But sometimes one person’s reality is different than another’s Sometimes we have no idea what reality is –The scene we photographed is long gone We need a way to quantify our findings

6. Statistics… Figures often beguile me, particularly when I have the arranging of them myself; in which case the remark attributed to Disraeli would often apply with justice and force: "There are three kinds of lies: lies, damned lies and statistics." – Mark Twain

7. Statistics Statistics can tell us a lot about an image –Quality of exposure –Image manipulations –Compression/quantization

8. Statistics But if we compute the statistics in the “usual way” all we get is a bunch more numbers to look at –Min –Max –Mean –Mode –Skew –Standard deviation –etc. A picture is worth a thousand words (or number in this case)

9. Histogram Pictorial depiction of image statistics

10. Histogram The pixels within an image are arranged in a spatially coherent manner –What does that mean? Their position in the image matters A histogram is a frequency distribution of the pixel values within an image –What does that mean? It depicts the number of times a particular pixel value occurs in the image

11. Histogram Mathematically speaking… In words: h(i) is the number of pixels in the image I who’s value is i It will contain an array of values, 1 for each possible pixel value K

12. Histogram The histogram does not contain any spatial information whatsoever! –Can you reconstruct the original image from the histogram? No, just like if I give you a bunch of statistics you can’t recreate the original dataset!

13. What can you do with a histogram? Image Acquisition – exposure Where the concentration of pixel values lie within the histogram Laymen’s (subjective) terms: how bright or dark is the image

14. Under exposed

15. Over exposed

16. Properly exposed

17. What can you do with a histogram? Image acquisition – contrast How much of the pixel value range is effectively used –Note that “effectively” is yet another subjective term Laymen’s (subjective) term: how foggy is the image

18. Low contrast

19. High contrast

20. “Good” (normal?) contrast

21. What can you do with a histogram? Image acquisition – dynamic range The number of distinct pixel values in the image Often times this dynamic range will consider how much “noise” (unstructured, unwanted, unintended, modifications of the pixel values) as part of the definition Laymen’s (subjective) term: how posterized or contoured is the image

22. Very, very low dynamic range

23. Low dynamic range

24. High dynamic range

25. A test image

26. Test image Exposure? Contrast? Dynamic range?

27. ImageJ 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