Image Processing Demonstration
Introduction -
Compression -
Plaids -
Tutorials
Introduction
When you eyes perceive an image, like a spectacular San Francisco sunset,
they receive light which falls on a hexagonal grid of receptors.
The brain processes this sampled two-dimensional signal to make sense
of the contents of the scene.
Similarly, in processing images on a computer, the images are first sampled,
but on a rectangular grid.
The spacing between grid points sets the resolution in the spatial and
frequency domains of the content in the image, just as it does in
speech signals.
In general, cameras often sample images at much higher resolution than
necessary to make out details.
The value at each grid point is the intensity at that grid point.
The intensity at each grid point is often stored
as an 8-bit integer in black-and-white images and
in 24 bits in color images (8 bits per ``primary'' color of red, green, blue).
For example, here's an example in which we have increased the resolution
(decrease the grid spacing) from left to right:
This picture is borrowed from the announcement for
ICASSP '96.
Image Compression
One common application of image processing is image compression.
In image compression, we seek to reduce the amount of information
to represent an image.
On Unix systems, file compression is used to compact files without
changing the file contents by removing redundant information.
This is known as lossless compression.
Another approach, known as lossy compression, discards some
information but maintains a certain quality.
JPEG and MPEG are lossy compression standards for image and video
signals, respectively.
Plaids
One approach for lossy compression is to take the image
and decompose it into components
that capture the gross properties of the image, while taking up
less space to store.
If we add a sufficient number of these components together, we
start to get something
We can tradeoff the number of components (i.e., storage size) vs.
the quality of the compressed image.
Last updated 10/02/95, Send comments to
ble@eecs.berkeley.edu