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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
• 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
Resolution is detail
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 imageis 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 makethe 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 oversamplingFor example ==>
If you cut each of the dimensionsin half (8x8 -> 4x4)=> 64 - 16 = 48 pixels removed
You have to remove 3/4 of the pixels!
64 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 informationin 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==>
Consider this image:
With no compression...
RGB encoding => 64 x 3 = 192 bytes
Data Compression
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 ==>
Consider this image:
64 pixels
0100100
Data CompressionTable (or dictionary)with just two colors:
25500
0 0 0 0 0 0 0 00 1 1 1 1 1 1 00 1 1 1 1 1 1 00 1 1 1 1 1 1 00 1 1 1 1 1 1 00 1 1 1 1 1 1 00 1 1 1 1 1 1 00 0 0 0 0 0 0 0
14 bytes
112 bits
==>or
Consider this image:
64 pixels
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
Data CompressionRun Length Encoding
Run Length Encoding
• This advantage would be dependent on the CONTENT of the image.
• Why?• Could RLE result in a larger image?• How?
Consider this image:
64 pixels
RLE compression...
1RGB1RGB1RGB1RGB1RGB... 1RGB1RGB1RGB-> 256 bytes
Run Length Encoding:Always better than RGB?
Run Length Encoding• RLE is Lossless
• What is lossless?
compressed
originalcompression routine
Original
decompress routine
OriginalExactduplicate
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 processingjust deal with a single pixel
• Pixel group processingthe 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
DownsamplingUpsampling
Pixel group processingDifferent ways to do this that result in different resultsNearest 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
Convolutionmask
X
Convolutionkernel
X
Using this convolution maskon this convolution kernelthe 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)
1/9 1/9 1/9
1/9 1/9 1/9
1/9 1/9 1/9
Convolutionmask
X
X
Using this convolution maskon this convolution kernelthe 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)
1/9 1/9 1/9
1/9 1/9 1/9
1/9 1/9 1/9
Convolutionmask
X
X
Using this convolution maskon this convolution kernelthe 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)
1/9 1/9 1/9
1/9 1/9 1/9
1/9 1/9 1/9
Convolutionmask
X
X
Using this convolution maskon this convolution kernelthe 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)
1/9 1/9 1/9
1/9 1/9 1/9
0/9 3/9 0/9
Using adifferent Convolutionmask...
X
X X X X
Convolution CalculationsPixel Group Processing
Next class we will do some more of these
Image Encoding PracticePowerpoint slides found on the wiki in
“additional class information”
Questions?
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