Joint Bi-level Image Experts Group

algorithm (JBIG) An experts group of ISO, IEC and

ITU-T (JTC1/SC2/WG9 and SGVIII) working to define a

compression standard for lossless image coding. Their

proposed algorithm features compatible progressive coding

and sequential coding and is lossless - the image is

unaltered after compression and decompression.

JBIG can handle images with from one to 255 bits per pixel.

Better compression algorithms exist for more than about eight

bits per pixel. With multiple bits per pixel, Gray code can

be used to reduce the number of bit changes between adjacent

decimal values (e.g. 127 and 128), and thus improve the

compression which JBIG does on each bitplane.

JBIG uses discrete steps of detail by successively doubling

the resolution. The sender computes a number of resolution

layers and transmits these starting at the lowest resolution.

Resolution reduction uses pixels in the high resolution layer

and some already computed low resolution pixels as an index

into a lookup table. The contents of this table can be

specified by the user.

Compatibility between progressive and sequential coding is

achieved by dividing an image into stripes. Each stripe is a

horizontal bar with a user definable height. Each stripe is

separately coded and transmitted, and the user can define in

which order stripes, resolutions and bitplanes are intermixed

in the coded data. A progressively coded image can be decoded

sequentially by decoding each stripe, beginning by the one at

the top of the image, to its full resolution, and then

proceeding to the next stripe. Progressive decoding can be

done by decoding only a specific resolution layer from all

stripes.

After dividing an image into bitplanes, resolution layers

and stripes, eventually a number of small bi-level bitmaps

are left to compress. Compression is done using a Q-coder.

The Q-coder codes bi-level pixels as symbols using the

probability of occurrence of these symbols in a certain

context. JBIG defines two kinds of context, one for the

lowest resolution layer (the base layer), and one for all

other layers (differential layers). Differential layer

contexts contain pixels in the layer to be coded, and in the

corresponding lower resolution layer.

For each combination of pixel values in a context, the

probability distribution of black and white pixels can be

different. In an all white context, the probability of coding

a white pixel will be much greater than that of coding a black

pixel. The Q-coder, like Huffman coding, achieves

compression by assigning more bits to less probable symbols.

The Q-coder can, unlike a Huffman coder, assign one output

code bit to more than one input symbol, and thus is able to

compress bi-level pixels without explicit clustering, as

would be necessary using a Huffman coder.

[What is "clustering"?]

Maximum compression will be achieved when all probabilities

(one set for each combination of pixel values in the context)

follow the probabilities of the pixels. The Q-coder therefore

continuously adapts these probabilities to the symbols it

sees.

JBIG can be regarded as two combined algorithms:

(1) Sending or storing multiple representations of images at

different resolutions with no extra storage cost.

Differential layer contexts contain pixels in two resolution

layers, and so enable the Q-coder to effectively code the

difference in information between the two layers, instead of

the information contained in every layer. This means that,

within a margin of approximately 5%, the number of resolution

layers doesn't effect the compression ratio.

(2) A very efficient compression algorithm, mainly for use

with bi-level images. Compared to CCITT Group 4, JBIG is

approximately 10% to 50% better on text and line art, and even

better on halftones. JBIG, just like Group 4, gives worse

compression in the presence of noise in images.

An example application would be browsing through an image

database.

["An overview of the basic principles of the Q-coder adaptive

binary arithmetic coder", W.B. Pennebaker, J.L. Mitchell,

G.G. Langdon, R.B. Arps, IBM Journal of research and

development, Vol.32, No.6, November 1988, pp. 771-726].

.

(1998-03-29)