[robski]

Image pixel data is stored in one of two formats. Contone (CONtinuous TONE) where data is stored in bytes to represent the different levels of brightness and colour. This is the type of data produced by a scanner or camera. For the purposes of printing newspapers and magazines on a printing press these are converted and stored in 1bit image format. Basically a stream of zeros and ones ( on - off signals if you like). Often referred to as 1bit tiff or bitmaps, Line Art and halftones images. The Lossless Group 3 and 4 compress 1bit data very well. It is the Contone data that presents the headache for compression.

Graphic image data with lots of fine detail when compressed using a lossless method entail lots of processing for little compression effect. New ideas were needed to overcome the problem which led to a detailed examination of the information stored in an image. An image is shades of light and dark of different hues. The viewer is the human eye and brain. The new ideas were centered around exploiting the strengths and weakness of the human system. What information does the eye and brain need to see the image correctly? What detail can we afford to lose without it being noticed and what detail lost will be noticed ?

The brain is a powerful tool and can workout a lot of things for itself.

"Digital imagi_g is rapidly replacing film phot_graphy in consumer _nd professional markets"

If you can read and understand English the message in the sentence above is not lost despite the fact 3 characters are missing.

One approach of removing information from graphic image data is to apply a process that will remove noise (seen as a fine grain effect) caused by the input sensor of a camera or scanner. By definition Noise is unwanted and random. The denoising process is designed to smooth out random pixel hot spots and leave behind the wanted picture information. A simple approach is to zero the least significant bit of a byte. Or in Plain English to round down all odd values held in a byte to the next even number. Sophisticated programs such as ninja, picture cooler and neat image analysis a patch of bland picture area to build a profile of the noise characteristics and apply it to the whole picture area. The overall effect is much the same in each case that it has smoothed out many of the little pits and bumps in the range of data values. Effectively reducing the number of changes (transitions) in value of the data. Changing the pixels colour value by 1/256th is not going to be noticed.

Example original raw data
ABABAABAEDEDDEKKLKKLLK

Compressed original ( in this case the code is longer )
1A1B1A1B2A1B1A1E1D1E2D1E2K1L2K2L1K

Smoothed data ( noise removed )
AAAAAAAAEEEEEEKKKKKKKK

Compressed Smoothed data
8A6E8K

The smoothing has increased the length of runs of data which will improve the compression ratio.

Of course this approach of throwing information away can only be applied to audio/visual data. Dangerous and senseless to throw away information in a spreadsheet of an accounts system.

Next we will turn our attention to JPEG. As yet nobody has managed to slap a license on its use so camera manufactures and photo editing programs can freely enjoy the benefits of this picture image format. In 1982 the ISO formed the Photographic Expert Group to research methods of transmitting Video and still images over data links. It's Goal was to produce a set of industry standards. In 1986 the CCITT set up a group to work on Compression methods required for colour facsimile machine transmission. In 1987 the two groups were combined to form a joint committee (Joint Photographic Experts Group ( JPEG )) that would research and produce a single standard.

JPEG unlike other compression methods is not a single algorithm but maybe thought of as a toolkit of image compression methods to suit the users needs.