Explain the theory of image, audio and video compression.
In This Chapter, you’ll learn on:
Describe raw multimedia data representation
Describe data compression for image, audio and
Describe lossy and lossless compression
Raw Multimedia Data Representation
What exactly is Raw Multimedia Data
Representation? In simple terms with reference to
Digital Imaging it literally means "raw" as in
"unprocessed". A RAW file contains the original
image information as it comes off the sensor before
in-camera processing so you can do that processing
afterwards on your PC with special software. RAW
files are huge in file size and contain redundant data
Compression for Image, Audio and Video
Data compression is the removal of redundant data. This,
therefore, reduces the number of binary ‘bits’ necessary to
represent the information contained within that data. To
achieve the best possible compression requires not only an
understanding of the nature of data in its binary
representation but also how we as humans interpret the
information that the data represents.
Data compression is the general term for the various
algorithms and programs developed to address this
problem. A compression program is used to convert data
from an easy-to-use format to one optimized for
There are a few different techniques involved in data
compression such as Run-Length, LZW & JPEG.
Lossy and Lossless Compression
The above techniques are either lossless or lossy
Lossless Compression is used when it is important that
the original and the decompressed data are exactly
identical, or when no assumption can be made on
whether certain deviation is uncritical.
This is a very simplistic approach that counts
sequences of repeating symbols — storing the
symbol’s value and the number of repeats.
Consider the following example:
Image 1 – Run-Length Encoding - Illustrates run-length
encoding for a data sequence having frequent runs of
zeros. Each time a zero is encountered in the input data,
two values are written to the output file. The first of these
values is a zero, a flag to indicate that run-length
compression is beginning. The second value is the
number of zeros in the run. If the average run-length is
longer than two, compression will take place. On the
other hand, many single zeros in the data can make the
encoded file larger than the original.
Another example of Lossless compression is LZW
LZW compression is named after its developers, A. Lempel and
J. Ziv, with later modifications by Terry A. Welch. It is the
foremost technique for general purpose data compression due
to its simplicity and versatility. Typically, you can expect LZW to
compress text, executable code, and similar data files to about
one-half their original size.
LZW compression is always used in GIF image files, and offered
as an option in TIFF and PostScript.
Image 2 – LZW Compression - Illustrates in the table the values
between 0-255, from 256 to 4095 any sequence of data is
translated to that number in the table.
As the image above applies the compression method to a
series of numbers, in image compression the LZW method works
by finding patterns of data to which it assigns codes. It works
best on highly patterned images.
Lossy Compression reduces a file by permanently eliminating
certain information, especially redundant information. When
the file is uncompressed, only a part of the original information
is still there (although the user may not notice it). Lossy
compression is generally used for video and sound, where a
certain amount of information loss will not be detected by most
users. The JPEG image file, commonly used for photographs
and other complex still images on the Web, is an image that
has lossy compression. Using JPEG compression, the creator
can decide how much loss to introduce and make a trade-off
between file size and image quality.