Digital Media
Dr. Jim Rowan
ITEC 2110
Bitmapped Images
Device Resolution
• Determines how finely the device
approximates the continuous phenomenon
• Can be expressed a number of different ways
– Number of dots per inch
– Number of pixels per inch
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
affects
Display Size
• Is it smaller than you thought?
• Same image, displayed at different resolutions
Image Formats
• The pixel x and y dimensions of the image
can be seen as a measure of how much
DETAIL is contained in the picture
• Most encode (by putting in the header) the
resolution of the image in Pixels Per Inch
(PPI)
• Many encode (by putting in the header) the
original size as pixel width and pixel height
Resolution Increase?
• Is image resolution lower than the
output device?
– Must scale it up...
– Must add pixels...
– Requires interpolation between pixels
For
example==>
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?
?
You can consider what
the colors are that surround
the original pixel
Mathematically this usually
takes the form of matrix operation
?
Resolution Decrease…
• 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
For example ==>
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
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 special effects
(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)
• Other data compression techniques ==>
Data Compression
Consider this image:
With no compression...
RGB encoding =>
64 x 3 = 192 bytes
64 pixels
Side Note
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 ===> 3 bytes
How many colors?
2*24 = 16,777,216 different colors
Data Compression
Table (or dictionary)
with just two colors:
Consider this image:
64 pixels
0
100
100
256
0
0
00000000
01111110
01111110
01111110
01111110
01111110
01111110
00000000
==>
14 bytes
or
112 bits
Data Compression
Run Length Encoding
Consider this image:
RLE compression...
9RGB6RGB2RGB6RGB2RGB
6RGB2RGB6RGB2RGB6RGB
2RGB6RGB9RGB
= 52 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.
Lossless techniques
Can be used on image files
color table can be lossless
tiff is lossless but it doesn’t compress
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
• Lossy
• Best suited for natural photographs and
similar images
– Fine details with continuous tone changes
• JPEG takes advantage 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...
• Discards most of the high frequency
components 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?
• HMMMmmmm…
One reason lossy
compression works
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 to work ==>
Optical Illusions
• See Additional Class Information: Illusions
Bitmapped image manipulation
• Like GIMP and Photoshop…
• Pixel point processing
• Pixel group processing
• 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
Remember this dilemma?
Rescaling a bitmapped image is called resampling:
Two kinds
Downsampling
Upsampling
Different ways to do this that result in different results
P111 Nearest Neighbor, bilinear a& 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
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
Convolution Calculations
Refer to additional information for
examples to be worked
Questions?