Source : Free On-Line Dictionary of Computing

Joint Bi-level Image Experts Group
     
         (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].
     
        {(http://www.crs4.it/~luigi/MPEG/jbig.html)}.
     
        (1998-03-29)