Digital Media

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Digital Media

• Dr. Jim Rowan
• ITEC 2110
• Bitmapped Images

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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

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Device Resolution affectsDisplay Size

• Is the image smaller than you thought?

• Same image, displayed at different resolutions

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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 gt

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












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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?












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















?
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Decrease Resolution

• Must discard some pixels...
• AKA downsampling
• Downsampling Presents a paradox
• There are fewer bits since youre 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 gt

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








64 pixels




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












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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 its just numbers
in there!
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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)

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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.

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Data Compression

• What weve 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 lets talk about compression techniquesgt

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Data Compression
Consider this image








With no compression... RGB encoding gt 64 x 3
192 bytes
64 pixels
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Side Note 1

• Weve been talking about RGB encoding for images
  
• So
• How many different colors can you make if using a
  24 bit RGB color scheme?

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Side Note 2

• 24 bits gt How many colors?
• 224 16,777,216 different colors
• Now back to compression techniques gt

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Data CompressionTable (or dictionary)with just
two colors
0 100 100
255 0 0
Consider this image








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
gt
or
64 pixels
112 bits
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Data CompressionRun Length Encoding
Consider this image
RLE compression... 9RGB6RGB2RGB6RGB2RGB6RGB2RGB6R
GB2RGB6RGB2RGB6RGB9RGB 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
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Run Length Encoding

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

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Run Length EncodingAlways better than RGB?
Consider this image








RLE compression... 1RGB1RGB1RGB1RGB1RGB...
1RGB1RGB1RGB -gt 256 bytes
64 pixels
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Run Length Encoding

• RLE is Lossless
• What is lossless?

compressed original
Exact duplicate
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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.

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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?

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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
  dont perceive the effect of high frequencies
  accurately
• (High frequency components are associated with
  abrupt changes in image intensity like a hard
  edge)

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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

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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
• gtJPEG uses more storage space for the more
  visible elements of an image

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JPEG compression...

• Lossy
• Effective for the kinds of images it is intended
  for gt 95 reduction in size
• Allows the control of degree of compression
• Suffers from artifacts that causes edges to
  blur... WHY?
• HMMMmmmm

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One reason lossy compression works we just dont
notice it!
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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 dont see what you think you see gt

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Optical Illusions

• See Additional Class Information Illusions

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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

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• 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

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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




















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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?
• gt Convolution!

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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 thisgt

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1/9 1/9 1/9
1/9 1/9 1/9
1/9 1/9 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






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1/9 1/9 1/9
1/9 1/9 1/9
1/9 1/9 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






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1/9 1/9 1/9
1/9 1/9 1/9
1/9 1/9 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






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1/9 1/9 1/9
1/9 1/9 1/9
1/9 1/9 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






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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






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Convolution Calculations

• Next class we will do some more of these

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Questions?