1. Dr. Jim Rowan ITEC 2110 Bitmapped Images
Digital Media
Dr. Jim Rowan
ITEC 2110
Bitmapped Images

2. Resolution is detail
The x and y dimensions of the image can be seen as a measure of how much DETAIL is contained in the picture
Many image formats encode these dimensions by putting in the header (as we saw in the first day demonstration)
The color depth determines how many colors this detail can assume

4. Device Resolution affects Display Size
Is the image smaller than you thought?
Same image, displayed at different resolutions

5. What happens when you increase resolution?
For example, to go from 72 dpi to 144 dpi
AKA upsampling
Must scale it up...
To do that, you must add pixels…
This requires interpolation between pixels
For example, to go from 4x4 to 8x8 ==>

6. Here the original 4x4 image is doubled in size to 8x8 by adding pixels
But this example is pretty simple because the original is all one color…

7. Well, the answer is… it depends!
If you double the image size
you have to add pixels...
But what color do you make
the additions? ?
Well, the answer is… it depends!

8. the colors are that surround the original pixel
You can consider what
the colors are that surround
the original pixel
Mathematically this usually
takes the form of matrix operation ?

9. Decrease Resolution… Must discard some pixels...
AKA downsampling
Downsampling: Presents a paradox
There are fewer bits since you’re throwing some pixels out
But... subjective quality goes up
How? Downsampling routine can use the tossed-out pixels to modify the remaining pixel
Intentionally doing this is called oversampling
For example ==>

10. How do you decide which pixels to remove?
If you cut each of the dimensions
in half (8x8 -> 4x4)=> =
48 pixels removed
You have to remove 3/4 of the pixels!
64 pixels
16 pixels
How do you decide which pixels to remove?

11. One answer: throw them away!
Here it works...
because it is a solid color

12. But what if it is multi-colored?
You can use the information
in the surrounding pixels to
influence the remaining pixel
How do you do this?
Remember… it’s just numbers in there!

13. Convolution Calculations
Convolution is the mathematical process that image software (like GIMP or Photoshop) use to do these things (More of this in the next lecture)

14. Browsers... generally bad at downsampling
Their image processing is not very sophisticated
What are the implications?
Use image processing programs to do downsampling
(GIMP, Photoshop) are sophisticated enough to take advantage of the extra information so...
Images for WWW should be downsampled before they are used on the web.

15. Data Compression What we’ve seen so far: How to reduce that?
Storing an image as an array of pixels
With color stored as three bytes per pixel
Image file gets BIG fast!
How to reduce that?
Using a color table reduces the file size of the stored image (as seen before)
So let’s talk about compression techniques==>

16. Data Compression Consider this image: With no compression...
RGB encoding =>
64 x 3 = 192 bytes
64 pixels

17. Side Note 1
We’ve been talking about RGB encoding for images… So… How many different colors can you make if using a 24 bit RGB color scheme?

18. Side Note 2
24 bits ===> How many colors? 2**24 = 16,777,216 different colors Now back to compression techniques ==>

19. Data Compression Table (or dictionary) with just two colors:
100
255
Consider this image:
14 bytes
==> or
64 pixels
112 bits

20. Data Compression Run Length Encoding
Consider this image:
RLE compression...
9RGB6RGB2RGB6RGB2RGB6RGB2RGB6RGB2RGB6RGB2RGB6RGB9RGB
9(0,0,255)6(255,0,0)2(0,0,255)6(255,0,0)2(0,0,255)6(255,0,0)2(0,0,255)6(255,0,0)2(0,0,255)6(255,0,0)2(0,0,255)6(255,0,0)9(0,0,255)
= 52 bytes
64 pixels

21. Run Length Encoding
This advantage would be dependent on the CONTENT of the image.
Why?
Could RLE result in a larger image?
How?

22. Run Length Encoding: Always better than RGB?
Consider this image:
RLE compression...
1RGB1RGB1RGB1RGB1RGB... 1RGB1RGB1RGB
-> 256 bytes
64 pixels

23. Run Length Encoding RLE is Lossless What is lossless? Original Exact
compression routine
Original
compressed
original
decompress routine
Exact
duplicate
Original

24. Dictionary-based (aka Table-based) compression technique
(Note: Data compression works on files other than images)
Construct a table of strings (for images, colors) found in the file to be compressed
Each occurrence in the file of a string (for images, color) found in the table is replaced by a pointer to that occurrence.

25. Lossless techniques Can be used on image files
color table can be lossless
(if the color table holds all colors in the image)
One lossless technique is a zip file
Run length encoding is also lossless
A lossless technique must be used for executable files
Why?

26. JPEG compression JPG is Lossy
Best suited for natural photographs and similar images
Fine details with continuous tone changes
JPEG takes advantage of the fact that humans don’t perceive the effect of high frequencies accurately
(High frequency components are associated with abrupt changes in image intensity… like a hard edge)

27. JPEG compression... JPEG finds these high frequency components by
treating the image as a matrix
using the Discrete Cosine Transform (DCT) to convert an array of pixels into an array of coefficients
DCT is expensive computationally so it the image is broken into 8x8 pixel squares and applied to each of the squares

28. JPEG compression...
The high frequency components do not contribute much to the perceptible quality of the image
They encode the frequencies at different quantization levels giving the low frequency components greater detail
==>JPEG uses more storage space for the more visible elements of an image

29. JPEG compression... Lossy
Effective for the kinds of images it is intended for ==> 95% reduction in size
Allows the control of degree of compression
Suffers from artifacts that causes edges to blur... WHY?
HMMMmmmm…

30. One reason lossy compression works… we just don’t notice it!

31. Side Note! To make matters worse…
The human vision system is very complex
Upside down
Split- left side of eye to right side of brain
Right side of eye to left side of brain
Cones and rods not uniformly distributed
Cones and rods are upside down resulting in blind spots in each eye that we just ignore!
Partially responsible for making lossy techniques work… you don’t see what you think you see ==>

32. Optical Illusions
See Additional Class Information: Illusions

33. Bitmapped image manipulation
Like GIMP and Photoshop…
Pixel point processing
just deal with a single pixel
Pixel group processing
the single pixel is influenced by the pixels that surround it

34. Adjustment of color in an image is pixel point processing
Color adjustment
brightness
adjusts every pixel brightness up or down
contrast
adjusts the RANGE of brightness
increasing or reducing the difference between brightest and darkest areas

35. Pixel group processing
Rescaling a bitmapped image is called resampling: Two kinds
Downsampling
Upsampling
Pixel group processing
Different ways to do this that result in different results
Nearest Neighbor, bilinear & bicubic

37. Pixel Group Processing
Final value for a pixel is affected by its neighbors
Because the relationship between a pixel and its neighbors provides information about how color or brightness is changing in that region
How do you do this?
==> Convolution!

38. Convolution & Convolution Masks
Very expensive computationally
each pixel undergoes many arithmetic operations
If you want all the surrounding pixels to equally affect the pixel in question...
You need an image and a mask
Then apply the mask to the image
Visually it looks like this==>

39. 1/9 X
Convolution
mask
Convolution
kernel
Using this convolution mask
on this convolution kernel
the final value of the pixel (2,2)
will be:
pixel (2,2) = 1/9(1,1) + 1/9(1,2)+ 1/9(1,3)
+1/9(2,1) +1/9(2,2) +1/9(2,3)
+1/9(3,1) +1/9(3,2) +1/9(3,3) X

40. 1/9 X
Convolution
mask
Using this convolution mask
on this convolution kernel
the final value of the pixel (3,2)
will be:
pixel (3,2) = 1/9(1,2) + 1/9(1,3)+ 1/9(1,4)
+1/9(2,2) +1/9(2,3) +1/9(2,4)
+1/9(3,2) +1/9(3,3) +1/9(3,4) X

41. 1/9 X
Convolution
mask
Using this convolution mask
on this convolution kernel
the final value of the pixel (4,2)
will be:
pixel (4,2) = 1/9(1,3) + 1/9(1,4)+ 1/9(1,45)
+1/9(2,3) +1/9(2,4) +1/9(2,5)
+1/9(3,3) +1/9(3,4) +1/9(3,5) X

42. 1/9 X
Convolution
mask
Using this convolution mask
on this convolution kernel
the final value of the pixel (5,2)
will be:
pixel (5,2) = 1/9(1,4) + 1/9(1,5)+ 1/9(1,6)
+1/9(2,4) +1/9(2,5) +1/9(2,6)
+1/9(3,4) +1/9(3,5) +1/9(3,6) X

43. 1/9
0/9
3/9 X
Using a
different
Convolution
mask... X

44. Convolution Calculations Pixel Group Processing
Image Encoding Practice
Powerpoint slides found on the wiki in “additional class information”
Convolution Calculations Pixel Group Processing
Next class we will do some more of these

45. Questions?