04-07-2012, 11:40 AM
IMAGE COMPRESSION
[attachment=26626]
Introduction
A digital image obtained by sampling and quantizing a continuous tone picture requires an
enormous storage. For instance, a 24 bit color image with 512x512 pixels will occupy 768 Kbyte
storage on a disk, and a picture twice of this size will not fit in a single floppy disk. To transmit such
an image over a 28.8 Kbps modem would take almost 4 minutes. The purpose for image
compression is to reduce the amount of data required for representing sampled digital images and
therefore reduce the cost for storage and transmission. Image compression plays a key role in many
important applications, including image database, image communications, remote sensing (the use
of satellite imagery for weather and other earth-resource applications), document and medical
imaging, facsimile transmission (FAX), and the control of remotely piloted vehicles in military,
space, and hazardous waste control applications. In short, an ever-expanding number of applications
depend on the efficient manipulation, storage, and transmission of binary, gray-scale, or color
images.
An important development in image compression is the establishment of the JPEG standard for
compression of color pictures. Using the JPEG method, a 24 bit/pixel color images can be reduced
to between 1 to 2 bits/pixel, without obvious visual artifacts. Such reduction makes it possible to
store and transmit digital imagery with reasonable cost. It also makes it possible to download a color
photograph almost in an instant, making electronic publishing/advertising on the Web a reality.
Prior to this event, G3 and G4 standards have been developed for compression of facsimile
documents, reducing the time for transmitting one page of text from about 6 minutes to 1 minute.
Theories and Techniques for Image Compression
In general, coding method can be classified into Lossless and Lossy. With lossless coding, the
original sample values are retained exactly and compression is achieved by exploring the statistical
redundancies in the signal. With lossy coding, the original signal is altered to some extent to achieve
a higher compression radio.
Runlength Coding (RLC) of Bilevel Images
In one dimensional runlength coding of bilevel images, one scans the pixels from left to right
along each scan line. Assume that a line always starts and ends with white pixels, one counts the
number (referred to as runlength) of white pixels and that of the black pixels alternatively. The last
run of white pixels are replaced with a special symbol “EOL” (end of line). The runlengths of white
and black are coded using separate codebooks. The codebook, say, for the white runlengths is
designed using Huffman Coding method by treating each possible runlength (including EOL) as a
symbol.
Two Dimensional Runlength Coding
One dimensional runlength coding method only explores the correlation among pixels in the
same line. In two dimensional runlength coding or relative address coding, the correlation among
pixels in the current line as well as the previous line is explored. With this method, when a
transition in color occurs, the distance of this pixel to the most closest transition pixel (both before
and after this pixel) in the previous line as well as to the last transition pixel in the same line are
calculated, and the one with the shortest distance is coded, along with an index indicating which
type of distance is coded. See Fig. 6.17 in [1].
[attachment=26626]
Introduction
A digital image obtained by sampling and quantizing a continuous tone picture requires an
enormous storage. For instance, a 24 bit color image with 512x512 pixels will occupy 768 Kbyte
storage on a disk, and a picture twice of this size will not fit in a single floppy disk. To transmit such
an image over a 28.8 Kbps modem would take almost 4 minutes. The purpose for image
compression is to reduce the amount of data required for representing sampled digital images and
therefore reduce the cost for storage and transmission. Image compression plays a key role in many
important applications, including image database, image communications, remote sensing (the use
of satellite imagery for weather and other earth-resource applications), document and medical
imaging, facsimile transmission (FAX), and the control of remotely piloted vehicles in military,
space, and hazardous waste control applications. In short, an ever-expanding number of applications
depend on the efficient manipulation, storage, and transmission of binary, gray-scale, or color
images.
An important development in image compression is the establishment of the JPEG standard for
compression of color pictures. Using the JPEG method, a 24 bit/pixel color images can be reduced
to between 1 to 2 bits/pixel, without obvious visual artifacts. Such reduction makes it possible to
store and transmit digital imagery with reasonable cost. It also makes it possible to download a color
photograph almost in an instant, making electronic publishing/advertising on the Web a reality.
Prior to this event, G3 and G4 standards have been developed for compression of facsimile
documents, reducing the time for transmitting one page of text from about 6 minutes to 1 minute.
Theories and Techniques for Image Compression
In general, coding method can be classified into Lossless and Lossy. With lossless coding, the
original sample values are retained exactly and compression is achieved by exploring the statistical
redundancies in the signal. With lossy coding, the original signal is altered to some extent to achieve
a higher compression radio.
Runlength Coding (RLC) of Bilevel Images
In one dimensional runlength coding of bilevel images, one scans the pixels from left to right
along each scan line. Assume that a line always starts and ends with white pixels, one counts the
number (referred to as runlength) of white pixels and that of the black pixels alternatively. The last
run of white pixels are replaced with a special symbol “EOL” (end of line). The runlengths of white
and black are coded using separate codebooks. The codebook, say, for the white runlengths is
designed using Huffman Coding method by treating each possible runlength (including EOL) as a
symbol.
Two Dimensional Runlength Coding
One dimensional runlength coding method only explores the correlation among pixels in the
same line. In two dimensional runlength coding or relative address coding, the correlation among
pixels in the current line as well as the previous line is explored. With this method, when a
transition in color occurs, the distance of this pixel to the most closest transition pixel (both before
and after this pixel) in the previous line as well as to the last transition pixel in the same line are
calculated, and the one with the shortest distance is coded, along with an index indicating which
type of distance is coded. See Fig. 6.17 in [1].