Digital Media
Dr. Jim Rowan
ITEC 2110
Bitmapped Images
Device Resolution
• Determines how finely the device
approximates the continuous phenomenon
• Is closely related to sampling we discussed
earlier
• Can be expressed a number of different ways
– Printers and Scanners?
• Number of dots per inch
– Video?
• Number of pixels, pixel dimensions
• 320x160
Device Resolution
• When considering scanners and
printers pay attention to the resolution.
– The number of dots per inch a printer
produces will dictate the print size of the
image
– This can cause what appears to be a small
image to become quite large
Device Resolution and Printed
Size
• If the printer has a 72 dpi rating and the
image was scanned at 600 dpi, printing
the image (unscaled) will result in a
large image 600/72 = 8.33 times as
large
• To scale it to get the original size back
you would use a scaling factor of 72/600
or 0.12
Image Formats
• The pixel dimension of the image can be
seen as a measure of how much detail is
contained in the picture
• Most encode (put in the header) the
resolution of the image in Pixels Per Inch
(PPI)
• Many encode (put in the header) the original
size as pixel width and pixel height
Resolution Changes…
• Is image resolution lower than the the
output device?
– Must scale it up...
– Must add pixels...
– Requires interpolation between pixels
• Always results in quality reduction in
the image
Here the original 4x4 image
is doubled in size to
8x8 by adding pixels
If you double the image size
you have to add pixels...
But what color do you make
the additions?
?
Generally you consider what
the colors are that surround
the original pixel
Mathematically this usually
takes the form of matrix operation
?
Resolution reduction
• Is image resolution higher than the output device?
– Must discard some pixels...
– AKA downsampling
• Downsampling: 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
• How to do this? ==>
If you cut the image size in half
(8x8 -> 4x4)-> 64 - 16 = 48
pixels removed
64 pixels
You remove 3/4 of the pixels!
What do you do with thrown
away pixels?
16 pixels
One answer: throw them away!
Here it works...
because it is a solid color
Another answer:
Use the information
in the surrounding pixels to
influence the remaining pixel
Browsers...
really 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?
• Use a color table works to some degree
• Another way:
Use data compression techniques ==>
Data Compression
Consider this image:
With no compression...
RGB encoding =>
64 x 3 = 192 bytes
64 pixels
Data Compression
Run Length Encoding
Consider this image:
RLE compression...
9RGB6RGB2RGB6RGB2RGB
6RGB2RGB6RGB2RGB6RGB
2RGB6RGB9RGB
= 49 bytes
64 pixels
Run Length Encoding
• This advantage would be dependent on the
CONTENT of the image.
• Why?
• Could it result in a larger image?
• How?
• Generally, any data compression CAN result
in a larger file than using the pixel array
storage
– Dependent on the image contents
Run Length Encoding:
Always better than RGB?
Consider this image:
RLE compression...
1RGB1RGB1RGB1RGB1RGB.
.. 1RGB1RGB1RGB
-> 256 bytes
64 pixels
(a tiny lie!)
RGBRGBRGB... RGBRGB
-> 192 bytes
Run Length Encoding
• RLE is Lossless
• What is lossless?
Original
compression
routine
Exact
duplicate
Original
compressed
original
decompress
routine
Dictionary-based
(aka Table-based)
compression technique
• (Note: Data compression works on files other
than images)
• Construct a table of strings (colors) found in the
file to be compressed
• Each occurrence in the file of a string(color)
found in the table is replaced by a pointer to
that occurrence.
Data Compression
Dictionary-based(Table-based)
We’ve seen this!
Consider this image:
64 pixels
RGBRGB==>
[00000000][11111111]
[00000000].[11111111]
[00000000][00000000]
[00000000][01111110]
[01111110][01111110]
[01111110][01111110]
[01111110][00000000]
->14 bytes
Lossless techniques
Can be used on image files
Lossy techniques toss some data out
-jpeg is a lossy technique
• Must be used for executable files
• Why?
A Question:
• Making photorealistic animations look realistic
is very difficult...
• Why?
• 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!
• One result of which is optical illusions-->
Optical Illusions
• http://en.wikipedia.org/wiki/Same_color_illusi
on
• http://en.wikipedia.org/wiki/Grid_illusion
• http://en.wikipedia.org/wiki/Ponzo_illusion
• http://en.wikipedia.org/wiki/Image:Gradientoptical-illusion.svg
• http://en.wikipedia.org/wiki/Image:Revolving_
circles.svg
• http://blindspottest.com/
JPEG compression
• Best suited for photographs and similar
images
– Fine details with continuous tones
• Think of the array of pixels as a continuous
waveform with x&y with z being intensity
• High frequency components are associated
with abrupt changes in image intensity
• JPEG takes advantage that humans don’t
perceive the effect of high frequencies
accurately
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...
• DCT does not actually compress the image
• Allows most of the high frequency
components to be discarded because they do
not contribute much to the perceptible quality
of the image
• Encodes the frequencies at different
quantization levels giving the low frequency
components more quantization levels
• ==>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?
Image Manipulation
GIMP
• Why?
– Correct deficiencies (i.e. flash red eye)
• encapsulated sequence of operations to perform a
particular change
– Create images that are difficult or impossible to
create in nature
• special effects
• Create a WWW friendly image
– present an image in slices or in increasing
resolution as it loads on the web
Image Manipulation Tools
• Selection tools
– for regular shapes
• rectangular and elliptical marquee tools
• why is it called marquee?
– for irregular shapes
• lasso (polygon, magnetic, magic wand...)
– magnetic snaps to an enclosed object
using edge-detection routines
Selection tools...
• Allow the application of filters to only the
selected parts of the image
• The unaffected area is called a mask...
can be thought of as a stencil
• A 1-bit mask is either transparent or
opaque
• An 8-bit mask allows 256 levels of
transparency... AKA alpha channel
Selection tools...
• Making the mask with a gradient produces a
softer transition... a feathered edge.
• Can use anti-aliasing along the edge more
effectively hides the hard edge visually
• Layers can have masks associated with them
• Allows interesting compositing of image parts
Pixel Point Processing
• Allows adjustment of color in an image
• Color adjustment, linear
– brightness
• adjusts every pixel brightness up or down
– contrast
• adjusts the RANGE of brightness
• increasing or reducing the difference between
brightest and darkest areas
Open Image in GIMP...
Adjust levels
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... use
a evenly weighted convolution mask
1/9 1/9 1/9
1/9 1/9 1/9
X
1/9 1/9 1/9
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
1/9 1/9 1/9
1/9 1/9 1/9
X
1/9 1/9 1/9
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
1/9 1/9 1/9
1/9 1/9 1/9
X
1/9 1/9 1/9
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
1/9 1/9 1/9
1/9 1/9 1/9
X
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
1/9 1/9 1/9
1/9 1/9 1/9
X
0/9 3/9 0/9
Using a
different
Convolution
mask...
Homework:
What would be the
effect of this mask?
X
X
X
X
Questions?