Digital Color 24-bit Color Indexed Color Image file compression
Sharpening filters Slide 2 24-bit Pixels 24-bit color uses one byte
(8 bits) for each component of an RGB pixel (8 bits for red, 8 bits
for green, 8 bits for blue) The brightness for each color ranges
from 00000000=0 (darkest) 00000001=1 00000010=2 . 11111110=254
11111111=255 (brightest) Slide 3 24-bit Pixels Thus each of the
three colors can take on any of 256 different brightness levels The
total number of colors is 256x256x256 = approx 16.8 million colors
yellow pixel pink pixel Slide 4 Concept Question What color is a
pixel with values: Red: 0 Green: 255 Blue: 255 A.Blue B.Purple
C.Green D.Cyan E.Yellow 255 0 Slide 5 The Color Cube The color cube
is a way of visualizing the three- dimensional digital color space
http://www.danshort.com/colorcube/ Slide 6 Image Files: Size and
Resolution There are several different numbers to consider when
describing image size and resolution Pixel dimension: the number of
pixels in the image file itself, e.g. 1024x768 pixels The physical
size of an image, either on a screen or printed, depends on the
resolution of the image, the screen, and the printer Resolution is
typically given in pixels per inch Slide 7 Display Resolution
Computer and television displays have something called display
resolution Display resolution depends on the physical size of the
display and the pixel dimensions of the display You may see HDTVs
quoted as having 1920x1080 resolution, but this is a pixel
dimension, not a resolution. Slide 8 Screen Example Comparison 50
13 1280 pixels, 11 1920 pixels, 44 1920 pixels / 44 inches =
approx. 44 pixels per inch 1280 pixels / 11 inches = approx. 112
pixels per inch Slide 9 Print Resolution Print resolution, usually
given in dots per inch (dpi), is the number of individual colored
dots per inch that the printer produces on the paper In general,
you need at least 100 dpi, and preferably 300 dpi or more for an
image to look good when printed Combining the pixel dimension of
the image and the printing resolution gives the final physical size
of the image: e.g. a 1000x1000 pixel image printed at 250 dpi would
be 4x4 Slide 10 Image and Print Resolution Pixels per inch of the
image Dots per inch of a printed copy of the image Slide 11 Print
Resolution Slide 12 Concept Question An image has the pixel
dimension 1500x1500 pixels. It is printed at 300dpi. What is the
physical size of the printed image? A.3x3 B.3.5x3.5 C.4x4 D.4.5x4.5
E.5x5 Slide 13 Indexed Color A full 24-bit RGB color image would
require 3 bytes of storage for each pixel in the image, e.g. a 10
megapixel digital photograph would require 30 MB of storage space
This is, in general, not necessary. Most images do not contain 16.8
million different colors, so the file size can be reduced by
reducing the pixel depth For example, 8-bit color can only show 256
colors, but takes only 1/3 the space of a 24-bit color image Slide
14 Indexed Color While all 8-bit color images contain a maximum of
256 different colors, the colors themselves are not always the
same. When a 24-bit color image is converted to 8-bit color, a
color coding table is generated to include 256 colors out of the
16.8 million available to best preserve the color information in
the image Slide 15 Color Table Comparison Slide 16 Indexed Color
The color cube, containing all 16.8 million RGB colors, is divided
into 256 pieces to construct the color table for one particular
image Consider the color cube as a cube of cake containing an
evenly spaced array of 16.8 million poppyseeds, each representing
one color The colors that are actually present in the image are
represented by glowing poppyseeds Slide 17 Indexed Color The object
is to cut the cake into 256 pieces so that there are the same
number of glowing poppyseeds in each piece Each piece will contain
many glowing poppyseeds, but they will be very close in color, so
we can replace all those different colors with just one color for
each cake piece The cake cutting will be different for every
digital image Slide 18 Indexed Color: Color Tables Slide 19 Concept
Question A palette used for a computer image in an 8-bit color
scheme A.Contains 8 different colors, each chosen out of a possible
8 colors. B.Contains 8 different colors, each chosen out of a
possible 256 colors. C.Contains 256 different colors, each chosen
out of a possible 256 colors. D.Contains 256 different colors, each
chosen out of a possible 16.8 million colors. E.Contains 16.8
million colors. Slide 20 Storing Image Files The most nave way to
store an image is as a giant list of each pixel value in the image
This is very inefficient and results in very large image files
Image files can be made smaller by compression tricks. There are
two kinds of compression: Lossless compression: all information is
preserved, but compression is modest Lossy compression: compression
can be more extreme, but some information is throw away Slide 21
Lossless Compression Lossless compression preserves all the
information in an image file, but stores it in a more efficient
way. Different methods of lossless compression are designed to
efficiently compress different kinds of data The simplest lossless
image compression method is called run-length encoding Slide 22
Run-Length Encoding (RLE) RLE exploits the fact that there are
sections of repeated pixels in most images. Consider the following
sequence of pixels: This might represent the following colored
pixels: This can be expressed in fewer bits by replacing long runs
of the same color with two bytes, one indicating the color, and the
other indicating the number of repeats 000003284128 96745632 Slide
23 Run-Length Encoding 000003284128 96745632 Here we have coded
this in the following way: A red number indicates that the
following byte is to be read as a color, and repeated that number
of times A yellow number indicates the number of bytes following
which are nonrepeating. This (shorter) sequence of bytes is
re-expanded by the graphics software into the original sequence of
bytes 502328441283967456432 Slide 24 Concept Question For which
image would RLE produce a smaller file (better compression)
Original image Indexed Color: 16 colors AB Slide 25 Lossy
Compression Lossy compression makes image files smaller by both
storing information more efficiently, and throwing away
information. The most commonly used lossy compression method is
JPEG (Joint Photographic Experts Group) The JPEG method uses two
main compression steps: It merges similar colors into the same
color It removes small detail information from the image Slide 26
JPEG Compression: Color
http://clarebayley.com/2010/03/the-jpeg-compression-algorithm/
Slide 27 JPEG Compression: Detail There are more details about how
this works in the online notes The algorithm replaces blocks of
small details with the block from a library of possible blocks that
best matches the original image Slide 28 JPEG Compression: Results
Slide 29 JPEG Compression Slide 30 Image Filters: Sharpening
Sharpening filters in Photoshop work just like lateral inhibition
works in your retina. Edges between dark and light areas are
enhanced by making the light areas lighter and the dark areas
darker
http://micro.magnet.fsu.edu/primer/java/digitalimaging/processing/unsharpmask/
Slide 31 Sharpen Filter Image files are basically arrays (matrices)
of numbers, so image processing is just number processing Lets
consider the following mathematical filter: -12-1 Slide 32
Sharpening: Original 22222466666 Slide 33 Sharpening: Results
22220486666
