08-05-2012, 03:27 PM
IMAGE PROCESSING AND COMPRESSION TECHNIQUES
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An image defined in the "real world" is considered to be a function of two real
variables, for example, a(x,y) with a as the amplitude (e.g. brightness) of the image at the
real coordinate position (x,y).
An image may be considered to contain sub-images sometimes referred to as
regions-of-interest, ROIs, or simply regions. This concept reflects the fact that images
frequently contain collections of objects each of which can be the basis for a region. In a
sophisticated image processing system it should be possible to apply specific image
processing operations to selected regions. Thus one part of an image (region) might be
processed to suppress motion blur while another part might be processed to improve
color rendition.
Image quantization:
The step subsequent to sampling in image digitization is quantization. A quantizer
maps a continuous variable u into a discrete variable u1, which takes values from a finite
set {r1, r2… rn} of numbers. This mapping is generally a staircase function &
quantization rule is as follows:
Define {tk, k=1,…,L+1} as a set of increasing transition or decision levels with t1
and tL+1 as minimum and maximum values,respectively,of u. if u lies in intervals
[tk,tL+1], then it is mapped to rk, the kth reconstruction level.
Image enhancement:
It refers to accentuation, or sharpening, of image features such as boundaries, or
contrast to make a graphic display more useful for display & analysis. This process does
not increase the inherent information content in data.
It includes gray level & contrast manipulation, noise reduction, edge crispening
and sharpening, filtering, interpolation and magnification, pseudocoloring, and so on.
Image restoration:
It is concerned with filtering the observed image to minimize the effect of
degradations. Effectiveness of image restoration depends on the extent and accuracy of
the knowledge of degradation process as well as on filter design. Image restoration differs
from image enhancement in that the latter is concerned with more extraction or
accentuation of image features.
Lossless compression:
In this, data is not altered in process of compression or decompression.
Decompression generates an exact replica of an original image. “Text compression” is a
good example .spreadsheets, processor files usually contain repeated sequence of
characters. By reducing repeated characters to count, we can reduce requirement of bits.
Grayscale&images contain repetitive information .this repetitive graphic
images and sound allows replacement of bits by codes. In color images , adjacent pixels
can have different color values. These images do not have sufficient repetitiveness to be
compressed. In these cases, this technique is not applicable.