Digital Media
Dr. Jim Rowan
ITEC 2110
Bitmapped Images

Bitmapped images have problems scaling
Vector graphics do not have scaling issues

Bitmapped scaling
Vector graphic scaling

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

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

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 ==>

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…

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!

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

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 ==>

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

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

Another answer:
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!

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)

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.

Data Compression
• What we’ve seen so far:
– 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==>

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

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?

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

Data Compression
Table (or dictionary) with just two colors:
Consider this image:
0
100
100
255
0
0
64 pixels
0 0 0 0 0 0 0 0
0 1 1 1 1 1 1 0
0 1 1 1 1 1 1 0
0 1 1 1 1 1 1 0
0 1 1 1 1 1 1 0
0 1 1 1 1 1 1 0
0 1 1 1 1 1 1 0
0 0 0 0 0 0 0 0
==>
14 bytes or
112 bits

Data Compression
Run Length Encoding
Consider this image:
64 pixels
RLE compression...
9RGB6RGB2RGB6RGB2RGB
6RGB2RGB6RGB2RGB6RGB
2RGB6RGB9RGB
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

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

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

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

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.
• Lossless ONLY if all the colors are in the color table

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?

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)

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

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

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…

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

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 ==>

Optical Illusions
• See Additional Class Information: Illusions

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

• 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

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

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!

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==>

1/9 1/9 1/9
1/9 1/9 1/9
1/9 1/9 1/9
Convolution mask
Convolution kernel
X
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

1/9 1/9 1/9
1/9 1/9 1/9
1/9 1/9 1/9
Convolution mask
X
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

1/9 1/9 1/9
1/9 1/9 1/9
1/9 1/9 1/9
Convolution mask
X
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

1/9 1/9 1/9
1/9 1/9 1/9
1/9 1/9 1/9
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
X

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

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

Questions?