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 for JBIG
From V.E.R.A. -- Virtual Entity of Relevant Acronyms (February 2016) :

         Joint Bi-level Image expert Group (org., JTC1)

From The Free On-line Dictionary of Computing (30 December 2018) :

  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
     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
     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
     ["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].

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