From: jloup@chorus.fr (Jean-loup Gailly)
Newsgroups: comp.compression,news.answers
Subject: comp.compression Frequently Asked Questions (part 2/3)
Summary: *** READ THIS BEFORE POSTING ***
Keywords: data compression, FAQ
Date: 7 Jan 93 18:16:13 GMT
Expires: 15 Feb 93 16:17:20 GMT
Reply-To: jloup@chorus.fr
Followup-To: comp.compression
Supersedes: <compr2_5jan93@chorus.fr>

Archive-name: compression-faq/part2
Last-modified: Jan 5th, 1993

This file is part 2 of a set of Frequently Asked Questions for the
groups comp.compression and comp.compression.research.
If you did not get part 1 or 3, you can get them by ftp
on rtfm.mit.edu [18.72.1.58] in directory
   /pub/usenet/news.answers/compression-faq

It looks as if this part 2 (sent on Jan 5th) got lost again,
so I am sending it for the second time (Jan 7th). Sorry.

If you don't want to see this FAQ regularly, please add the subject
line to your kill file. If you have corrections or suggestions for
this FAQ, send them to Jean-loup Gailly <jloup@chorus.fr>.  Thank you.

Contents
========

Part 2: (Long) introductions to data compression techniques

[70] Introduction to data compression (long)
       Huffman and Related Compression Techniques
       Arithmetic Coding
       Substitutional Compressors
          The LZ78 family of compressors
          The LZ77 family of compressors

[71] Introduction to MPEG (long)
       What is MPEG?
       Does it have anything to do with JPEG?
       Then what's JBIG and MHEG?
       What has MPEG accomplished?
       So how does MPEG I work?
       What about the audio compression?
       So how much does it compress?
       What's phase II?
       When will all this be finished?
       How do I join MPEG?
       How do I get the documents, like the MPEG I draft?

[72] What is wavelet theory?
[73] What is the theoretical compression limit?
[74] Introduction to JBIG


Part 3: (Long) list of image compression hardware

[85] Image compression hardware
[99] Acknowledgments


Search for "Subject: [#]" to get to question number # quickly. Some news
readers can also take advantage of the message digest format used here.

------------------------------------------------------------------------------

Subject: [70] Introduction to data compression (long)


Written by Peter Gutmann <pgut1@cs.aukuni.ac.nz>.

 Huffman and Related Compression Techniques
 ------------------------------------------

  *Huffman compression* is a statistical data compression technique which 
gives a reduction in the average code length used to represent the symbols of 
a alphabet.  The Huffman code is an example of a code which is optimal in the 
case where all symbols probabilities are integral powers of 1/2.  A Huffman 
code can be built in the following manner:

  (1) Rank all symbols in order of probability of occurrence.
    
  (2) Successively combine the two symbols of the lowest probability to form
      a new composite symbol; eventually we will build a binary tree where
      each node is the probability of all nodes beneath it.

  (3) Trace a path to each leaf, noticing the direction at each node.

  For a given frequency distribution, there are many possible Huffman codes,
but the total compressed length will be the same. It is possible to
define a 'canonical' Huffman tree, that is, pick one of these alternative
trees. Such a canonical tree can then be represented very compactly, by
transmitting only the bit length of each code. This technique is used
in most archivers (pkzip, lha, zoo, arj, ...).


  A technique related to Huffman coding is *Shannon-Fano coding*, which was 
suggested by Shannon and Weaver in 1949 and modified by Fano in 1961.  It 
works as follows:

  (1) Divide the set of symbols into two equal or almost equal subsets
      based on the probability of occurrence of characters in each
      subset.  The first subset is assigned a binary zero, the second
      a binary one.

  (2) Repeat step (1) until all subsets have a single element.

The algorithm used to create the Huffman codes is bottom-up, and the
one for the Shannon-Fano codes is top-down. Huffman encoding always
generates optimal codes, Shannon-Fano sometimes uses a few more bits.


 Arithmetic Coding
 -----------------

  It would appear that Huffman or Shannon-Fano coding is the perfect
means of compressing data.  However, this is *not* the case.  As
mentioned above, these coding methods are optimal when and only when
the symbol probabilities are integral powers of 1/2, which is usually
not the case.

  The technique of *arithmetic coding* does not have this restriction:
It achieves the same effect as treating the message as one single unit
(a technique which would, for Huffman coding, require enumeration of
every single possible message), and thus attains the theoretical
entropy bound to compression efficiency for any source.

  Arithmetic coding works by representing a number by an interval of real 
numbers between 0 and 1.  As the message becomes longer, the interval needed 
to represent it becomes smaller and smaller, and the number of bits needed to 
specify that interval increases.  Successive symbols in the message reduce 
this interval in accordance with the probability of that symbol. The more
likely symbols reduce the range by less, and thus add fewer bits to the   
message.

     1                                             Codewords
    +-----------+-----------+-----------+           /-----\
    |           |8/9 YY     |  Detail   |<- 31/32    .11111
    |           +-----------+-----------+<- 15/16    .1111
    |    Y      |           | too small |<- 14/16    .1110
    |2/3        |    YX     | for text  |<- 6/8      .110
    +-----------+-----------+-----------+
    |           |           |16/27 XYY  |<- 10/16    .1010
    |           |           +-----------+
    |           |    XY     |           |
    |           |           |   XYX     |<- 4/8      .100
    |           |4/9        |           |
    |           +-----------+-----------+
    |           |           |           |
    |    X      |           |   XXY     |<- 3/8      .011
    |           |           |8/27       |
    |           |           +-----------+
    |           |    XX     |           |
    |           |           |           |<- 1/4      .01
    |           |           |   XXX     |
    |           |           |           |
    |0          |           |           |
    +-----------+-----------+-----------+

  As an example of arithmetic coding, lets consider the example of two
symbols X and Y, of probabilities 0.66 and 0.33. To encode this message, we
examine the first symbol: If it is a X, we choose the lower partition; if
it is a Y, we choose the upper partition.  Continuing in this manner for
three symbols, we get the codewords shown to the right of the diagram above
- they can be found by simply taking an appropriate location in the
interval for that particular set of symbols and turning it into a binary
fraction. In practice, it is also necessary to add a special end-of-data
symbol, which is not represented in this simpe example.
        
  In this case the arithmetic code is not completely efficient, which is due 
to the shortness of the message - with longer messages the coding efficiency 
does indeed approach 100%.

  Now that we have an efficient encoding technique, what can we do with it? 
What we need is a technique for building a model of the data which we can 
then use with the encoder.  The simplest model is a fixed one, for example a 
table of standard letter frequencies for English text which we can then use 
to get letter probabilities.  An improvement on this technique is to use an 
*adaptive model*, in other words a model which adjusts itself to the data 
which is being compressed as the data is compressed.  We can convert the 
fixed model into an adaptive one by adjusting the symbol frequencies after 
each new symbol is encoded, allowing the model to track the data being 
transmitted.  However, we can do much better than that.

Using the symbol probabilities by themselves is not a particularly good
estimate of the true entropy of the data: We can take into account
intersymbol probabilities as well.  The best compressors available today
take this approach: DMC (Dynamic Markov Coding) starts with a zero-order
Markov model and gradually extends this initial model as compression
progresses; PPM (Prediction by Partial Matching) looks for a match of the
text to be compressed in an order-n context.  If no match is found, it
drops to an order n-1 context, until it reaches order 0.  Both these
techniques thus obtain a much better model of the data to be compressed,
which, combined with the use of arithmetic coding, results in superior
compression performance.

  So if arithmetic coding-based compressors are so powerful, why are they not 
used universally?  Apart from the fact that they are relatively new and 
haven't come into general use too much yet, there is also one major concern:  
The fact that they consume rather large amounts of computing resources, both 
in terms of CPU power and memory.  The building of sophisticated models for 
the compression can chew through a fair amount of memory (especially in the 
case of DMC, where the model can grow without bounds); and the arithmetic 
coding itself involves a fair amount of number crunching.
There is however an alternative approach, a class of compressors generally 
referred to as *substitutional* or *dictionary-based compressors*.

 Substitutional Compressors
 --------------------------

  The basic idea behind a substitutional compressor is to replace an 
occurrence of a particular phrase or group of bytes in a piece of data with a 
reference to a previous occurrence of that phrase.  There are two main 
classes of schemes, named after Jakob Ziv and Abraham Lempel, who first 
proposed them in 1977 and 1978.

<The LZ78 family of compressors>

  LZ78-based schemes work by entering phrases into a *dictionary* and then, 
when a repeat occurrence of that particular phrase is found, outputting the 
dictionary index instead of the phrase.  There exist several compression 
algorithms based on this principle, differing mainly in the manner in which 
they manage the dictionary.  The most well-known scheme (in fact the most 
well-known of all the Lempel-Ziv compressors, the one which is generally (and 
mistakenly) referred to as "Lempel-Ziv Compression"), is Terry Welch's LZW 
scheme, which he designed in 1984 for implementation in hardware for high- 
performance disk controllers.

Input string: /WED/WE/WEE/WEB

Character input:    Code output:    New code value and associated string:
    /W                  /                   256 = /W
    E                   W                   257 = WE
    D                   E                   258 = ED
    /                   D                   259 = D/
    WE                  256                 260 = /WE
    /                   E                   261 = E/
    WEE                 260                 262 = /WEE
    /W                  261                 263 = E/W
    EB                  257                 264 = WEB
    <END>               B
    
  LZW starts with a 4K dictionary, of which entries 0-255 refer to individual 
bytes, and entries 256-4095 refer to substrings.  Each time a new code is 
generated it means a new string has been parsed.  New strings are generated 
by appending the current character K to the end of an existing string w.  The 
algorithm for LZW compression is as follows:

  set w = NIL
  loop
      read a character K
      if wK exists is in the dictionary
          w = wK
      else
          output the code for w
          add wK to the string table
          w = K
  endloop

  A sample run of LZW over a (highly redundant) input string can be seen in 
the diagram above.  The strings are built up character-by-character starting 
with a code value of 256.  LZW decompression takes the stream of codes and 
uses it to exactly recreate the original input data.  Just like the 
compression algorithm, the decompressor adds a new string to the dictionary 
each time it reads in a new code.  All it needs to do in addition is to 
translate each incoming code into a string and send it to the output.  A 
sample run of the LZW decompressor is shown in below.

Input code: /WED<256>E<260><261><257>B

Input code:        Output string:     New code value and associated string:
    /                  /            
    W                  W                      256 = /W
    E                  E                      257 = WE
    D                  D                      258 = ED
    256                /W                     259 = D/
    E                  E                      260 = /WE
    260                /WE                    261 = E/
    261                E/                     262 = /WEE
    257                WE                     263 = E/W
    B                  B                      264 = WEB
           
  The most remarkable feature of this type of compression is that the entire 
dictionary has been transmitted to the decoder without actually explicitly 
transmitting the dictionary.  At the end of the run, the decoder will have a 
dictionary identical to the one the encoder has, built up entirely as part of 
the decoding process.
    LZW is more commonly encountered today in a variant known as LZC, after 
its use in the UNIX "compress" program.  In this variant, pointers do not 
have a fixed length.  Rather, they start with a length of 9 bits, and then 
slowly grow to their maximum possible length once all the pointers of a 
particular size have been used up.  Furthermore, the dictionary is not frozen 
once it is full as for LZW - the program continually monitors compression 
performance, and once this starts decreasing the entire dictionary is 
discarded and rebuilt from scratch.  More recent schemes use some sort of 
least-recently-used algorithm to discard little-used phrases once the 
dictionary becomes full rather than throwing away the entire dictionary.  

Finally, not all schemes build up the dictionary by adding a single new 
character to the end of the current phrase. An alternative technique is to 
concatenate the previous two phrases (LZMW), which results in a faster 
buildup of longer phrases than the character-by-character buildup of the 
other methods.  The disadvantage of this method is that a more sophisticated 
data structure is needed to handle the dictionary.

[A good introduction to LZW, MW, AP and Y coding is given in the yabba
package. For ftp information, see question 2 in part one, file type .Y]


<The LZ77 family of compressors>

  LZ77-based schemes keep track of the last n bytes of data seen, and when a 
phrase is encountered that has already been seen, they output a pair of 
values corresponding to the position of the phrase in the previously-seen 
buffer of data, and the length of the phrase.  In effect the compressor moves 
a fixed-size *window* over the data (generally referred to as a *sliding 
window*), with the position part of the (position, length) pair referring to 
the position of the phrase within the window.  The most commonly used 
algorithms are derived from the LZSS scheme described by James Storer and 
Thomas Szymanski in 1982.  In this the compressor maintains a window of size 
N bytes and a *lookahead buffer* the contents of which it tries to find a 
match for in the window:

  while( lookAheadBuffer not empty )
      {
      get a pointer ( position, match ) to the longest match in the window
          for the lookahead buffer;

      if( length > MINIMUM_MATCH_LENGTH )
          {
          output a ( position, length ) pair;
          shift the window length characters along;
          }
      else
          {
          output the first character in the lookahead buffer;
          shift the window 1 character along;
          }
      }
        
  Decompression is simple and fast:  Whenever a ( position, length ) pair is 
encountered, go to that ( position ) in the window and copy ( length ) bytes 
to the output.

  Sliding-window-based schemes can be simplified by numbering the input text
characters mod N, in effect creating a circular buffer.  The sliding window
approach automatically creates the LRU effect which must be done explicitly in
LZ78 schemes.  Variants of this method apply additional compression to the
output of the LZSS compressor, which include a simple variable-length code
(LZB), dynamic Huffman coding (LZH), and Shannon-Fano coding (ZIP 1.x)), all
of which result in a certain degree of improvement over the basic scheme,
especially when the data are rather random and the LZSS compressor has little
effect.
  Recently an algorithm was developed which combines the ideas behind LZ77 and
LZ78 to produce a hybrid called LZFG.  LZFG uses the standard sliding window,
but stores the data in a modified trie data structure and produces as output
the position of the text in the trie.  Since LZFG only inserts complete
*phrases* into the dictionary, it should run faster than other LZ77-based
compressors.

All popular archivers (arj, lha, zip, zoo) are variations on the LZ77 theme.

------------------------------------------------------------------------------

Subject: [71] Introduction to MPEG (long)


Written by Mark Adler <madler@cco.caltech.edu>.

Q. What is MPEG?
A. MPEG is a group of people that meet under ISO (the International
   Standards Organization) to generate standards for digital video
   (sequences of images in time) and audio compression.  In particular,
   they define a compressed bit stream, which implicitly defines a
   decompressor.  However, the compression algorithms are up to the
   individual manufacturers, and that is where proprietary advantage
   is obtained within the scope of a publicly available international
   standard.  MPEG meets roughly four times a year for roughly a week
   each time.  In between meetings, a great deal of work is done by
   the members, so it doesn't all happen at the meetings.  The work
   is organized and planned at the meetings.

Q. So what does MPEG stand for?
A. Moving Pictures Experts Group.

Q. Does it have anything to do with JPEG?
A. Well, it sounds the same, and they are part of the same subcommittee
   of ISO along with JBIG and MHEG, and they usually meet at the same
   place at the same time.  However, they are different sets of people
   with few or no common individual members, and they have different
   charters and requirements.  JPEG is for still image compression.

Q. Then what's JBIG and MHEG?
A. Sorry I mentioned them. Ok, I'll simply say that JBIG is for binary
   image compression (like faxes), and MHEG is for multi-media data
   standards (like integrating stills, video, audio, text, etc.).
   For an introduction to JBIG, see question 74 below.

Q. Ok, I'll stick to MPEG.  What has MPEG accomplished?
A. So far (as of January 1992), they have completed the "Committee
   Draft" of MPEG phase I, colloquially called MPEG I.  It defines
   a bit stream for compressed video and audio optimized to fit into
   a bandwidth (data rate) of 1.5 Mbits/s.  This rate is special
   because it is the data rate of (uncompressed) audio CD's and DAT's.
   The draft is in three parts, video, audio, and systems, where the
   last part gives the integration of the audio and video streams
   with the proper timestamping to allow synchronization of the two.
   They have also gotten well into MPEG phase II, whose task is to
   define a bitstream for video and audio coded at around 3 to 10
   Mbits/s.

Q. So how does MPEG I work?
A. First off, it starts with a relatively low resolution video
   sequence (possibly decimated from the original) of about 352 by
   240 frames by 30 frames/s (US--different numbers for Europe),
   but original high (CD) quality audio.  The images are in color,
   but converted to YUV space, and the two chrominance channels
   (U and V) are decimated further to 176 by 120 pixels.  It turns
   out that you can get away with a lot less resolution in those
   channels and not notice it, at least in "natural" (not computer
   generated) images.

   The basic scheme is to predict motion from frame to frame in the
   temporal direction, and then to use DCT's (discrete cosine
   transforms) to organize the redundancy in the spatial directions.
   The DCT's are done on 8x8 blocks, and the motion prediction is
   done in the luminance (Y) channel on 16x16 blocks.  In other words,
   given the 16x16 block in the current frame that you are trying to
   code, you look for a close match to that block in a previous or
   future frame (there are backward prediction modes where later
   frames are sent first to allow interpolating between frames).
   The DCT coefficients (of either the actual data, or the difference
   between this block and the close match) are "quantized", which
   means that you divide them by some value to drop bits off the
   bottom end.  Hopefully, many of the coefficients will then end up
   being zero.  The quantization can change for every "macroblock"
   (a macroblock is 16x16 of Y and the corresponding 8x8's in both
   U and V).  The results of all of this, which include the DCT
   coefficients, the motion vectors, and the quantization parameters
   (and other stuff) is Huffman coded using fixed tables.  The DCT
   coefficients have a special Huffman table that is "two-dimensional"
   in that one code specifies a run-length of zeros and the non-zero
   value that ended the run.  Also, the motion vectors and the DC
   DCT components are DPCM (subtracted from the last one) coded.

Q. So is each frame predicted from the last frame?
A. No.  The scheme is a little more complicated than that.  There are
   three types of coded frames.  There are "I" or intra frames.  They
   are simply a frame coded as a still image, not using any past
   history.  You have to start somewhere.  Then there are "P" or
   predicted frames.  They are predicted from the most recently
   reconstructed I or P frame.  (I'm describing this from the point
   of view of the decompressor.)  Each macroblock in a P frame can
   either come with a vector and difference DCT coefficients for a
   close match in the last I or P, or it can just be "intra" coded
   (like in the I frames) if there was no good match.

   Lastly, there are "B" or bidirectional frames.  They are predicted
   from the closest two I or P frames, one in the past and one in the
   future.  You search for matching blocks in those frames, and try
   three different things to see which works best.  (Now I have the
   point of view of the compressor, just to confuse you.)  You try using
   the forward vector, the backward vector, and you try averaging the
   two blocks from the future and past frames, and subtracting that from
   the block being coded.  If none of those work well, you can intra-
   code the block.

   The sequence of decoded frames usually goes like:

   IBBPBBPBBPBBIBBPBBPB...

   Where there are 12 frames from I to I (for US and Japan anyway.)
   This is based on a random access requirement that you need a
   starting point at least once every 0.4 seconds or so.  The ratio
   of P's to B's is based on experience.

   Of course, for the decoder to work, you have to send that first
   P *before* the first two B's, so the compressed data stream ends
   up looking like:

   0xx312645...

   where those are frame numbers.  xx might be nothing (if this is
   the true starting point), or it might be the B's of frames -2 and
   -1 if we're in the middle of the stream somewhere.

   You have to decode the I, then decode the P, keep both of those
   in memory, and then decode the two B's.  You probably display the
   I while you're decoding the P, and display the B's as you're
   decoding them, and then display the P as you're decoding the next
   P, and so on.

Q. You've got to be kidding.
A. No, really!

Q. Hmm.  Where did they get 352x240?
A. That derives from the CCIR-601 digital television standard which
   is used by professional digital video equipment.  It is (in the US)
   720 by 243 by 60 fields (not frames) per second, where the fields
   are interlaced when displayed.  (It is important to note though
   that fields are actually acquired and displayed a 60th of a second
   apart.)  The chrominance channels are 360 by 243 by 60 fields a
   second, again interlaced.  This degree of chrominance decimation
   (2:1 in the horizontal direction) is called 4:2:2.  The source
   input format for MPEG I, called SIF, is CCIR-601 decimated by 2:1
   in the horizontal direction, 2:1 in the time direction, and an
   additional 2:1 in the chrominance vertical direction.  And some
   lines are cut off to make sure things divide by 8 or 16 where
   needed.

Q. What if I'm in Europe?
A. For 50 Hz display standards (PAL, SECAM) change the number of lines
   in a field from 243 or 240 to 288, and change the display rate to
   50 fields/s or 25 frames/s.  Similarly, change the 120 lines in
   the decimated chrominance channels to 144 lines.  Since 288*50 is
   exactly equal to 240*60, the two formats have the same source data
   rate.

Q. You didn't mention anything about the audio compression.
A. Oh, right.  Well, I don't know as much about the audio compression.
   Basically they use very carefully developed psychoacoustic models
   derived from experiments with the best obtainable listeners to
   pick out pieces of the sound that you can't hear.  There are what
   are called "masking" effects where, for example, a large component
   at one frequency will prevent you from hearing lower energy parts
   at nearby frequencies, where the relative energy vs. frequency
   that is masked is described by some empirical curve.  There are
   similar temporal masking effects, as well as some more complicated
   interactions where a temporal effect can unmask a frequency, and
   vice-versa.

   The sound is broken up into spectral chunks with a hybrid scheme
   that combines sine transforms with subband transforms, and the
   psychoacoustic model written in terms of those chunks.  Whatever
   can be removed or reduced in precision is, and the remainder is
   sent.  It's a little more complicated than that, since the bits
   have to be allocated across the bands.  And, of course, what is
   sent is entropy coded.

Q. So how much does it compress?
A. As I mentioned before, audio CD data rates are about 1.5 Mbits/s.
   You can compress the same stereo program down to 256 Kbits/s with
   no loss in discernable quality.  (So they say.  For the most part
   it's true, but every once in a while a weird thing might happen
   that you'll notice.  However the effect is very small, and it takes
   a listener trained to notice these particular types of effects.)
   That's about 6:1 compression.  So, a CD MPEG I stream would have
   about 1.25 MBits/s left for video.  The number I usually see though
   is 1.15 MBits/s (maybe you need the rest for the system data
   stream).  You can then calculate the video compression ratio from
   the numbers here to be about 26:1.  If you step back and think
   about that, it's little short of a miracle.  Of course, it's lossy
   compression, but it can be pretty hard sometimes to see the loss,
   if you're comparing the SIF original to the SIF decompressed.  There
   is, however, a very noticeable loss if you're coming from CCIR-601
   and have to decimate to SIF, but that's another matter.  I'm not
   counting that in the 26:1.

   The standard also provides for other bit rates ranging from 32Kbits/s
   for a single channel, up to 448 Kbits/s for stereo.

Q. What's phase II?
A. As I said, there is a considerable loss of quality in going from
   CCIR-601 to SIF resolution.  For entertainment video, it's simply
   not acceptable.  You want to use more bits and code all or almost
   all the CCIR-601 data.  From subjective testing at the Japan
   meeting in November 1991, it seems that 4 MBits/s can give very
   good quality compared to the original CCIR-601 material.  The
   objective of phase II is to define a bit stream optimized for these
   resolutions and bit rates.

Q. Why not just scale up what you're doing with MPEG I?
A. The main difficulty is the interlacing.  The simplest way to extend
   MPEG I to interlaced material is to put the fields together into
   frames (720x486x30/s).  This results in bad motion artifacts that
   stem from the fact that moving objects are in different places
   in the two fields, and so don't line up in the frames.  Compressing
   and decompressing without taking that into account somehow tends to
   muddle the objects in the two different fields.

   The other thing you might try is to code the even and odd field
   streams separately.  This avoids the motion artifacts, but as you
   might imagine, doesn't get very good compression since you are not
   using the redundancy between the even and odd fields where there
   is not much motion (which is typically most of image).

   Or you can code it as a single stream of fields.  Or you can
   interpolate lines.  Or, etc. etc.  There are many things you can
   try, and the point of MPEG II is to figure out what works well.
   MPEG II is not limited to consider only derivations of MPEG I.
   There were several non-MPEG I-like schemes in the competition in
   November, and some aspects of those algorithms may or may not
   make it into the final standard for entertainment video compression.

Q. So what works?
A. Basically, derivations of MPEG I worked quite well, with one that
   used wavelet subband coding instead of DCT's that also worked very
   well.  Also among the worked-very-well's was a scheme that did not
   use B frames at all, just I and P's.  All of them, except maybe one,
   did some sort of adaptive frame/field coding, where a decision is
   made on a macroblock basis as to whether to code that one as one
   frame macroblock or as two field macroblocks.  Some other aspects
   are how to code I-frames--some suggest predicting the even field
   from the odd field.  Or you can predict evens from evens and odds
   or odds from evens and odds or any field from any other field, etc.

Q. So what works?
A. Ok, we're not really sure what works best yet.  The next step is
   to define a "test model" to start from, that incorporates most of
   the salient features of the worked-very-well proposals in a
   simple way.  Then experiments will be done on that test model,
   making a mod at a time, and seeing what makes it better and what
   makes it worse.  Example experiments are, B's or no B's, DCT vs.
   wavelets, various field prediction modes, etc.  The requirements,
   such as implementation cost, quality, random access, etc. will all
   feed into this process as well.

Q. When will all this be finished?
A. I don't know.  I'd have to hope in about a year or less.

Q. How do I join MPEG?
A. You don't join MPEG.  You have to participate in ISO as part of a
   national delegation.  How you get to be part of the national
   delegation is up to each nation.  I only know the U.S., where you
   have to attend the corresponding ANSI meetings to be able to
   attend the ISO meetings.  Your company or institution has to be
   willing to sink some bucks into travel since, naturally, these
   meetings are held all over the world.  (For example, Paris,
   Santa Clara, Kurihama Japan, Singapore, Haifa Israel, Rio de
   Janeiro, London, etc.)

Q. Well, then how do I get the documents, like the MPEG I draft?
A. MPEG is a draft ISO standard. It's exact name is ISO CD 11172.
   The draft consists of three parts: System, Video, and Audio. The
   System part (11172-1) deals with synchronization and multiplexing
   of audio-visual information, while the Video (11172-2) and Audio
   part (11172-3) address the video and the audio compression techniques
   respectively.

   You may order it from your national standards body (e.g. ANSI in
   the USA) or buy it from companies like
     OMNICOM
     phone +44 438 742424
     FAX +44 438 740154

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Subject: [72] What is wavelet theory?


Preprints and software are available by anonymous ftp from the
Yale Mathematics Department computer ceres.math.yale.edu[130.132.23.22],
in pub/wavelets and pub/software.

epic is a pyramid wavelet coder. (For source code, see item 15 in part one).

Bill Press of Harvard/CfA has made some things available for anonymous
ftp on cfata4.harvard.edu [128.103.40.79] in directory /pub. There is
a short TeX article on wavelet theory (wavelet.tex, to be included in
a future edition of Numerical Recipes), some sample wavelet code
(wavelet.f, in FORTRAN - sigh), and a beta version of an astronomical
image compression program which he is currently developing (FITS
format data files only, in fitspress08.tar.Z).

An experimental wavelet decomposition program is available
in linc.cis.upenn.edu:/pub/grasp/wavelet.tar.Z.

A mailing list dedicated to research on wavelets has been set up at the
University of South Carolina. To subscribe to this mailing list, send a
message with "subscribe" as the subject to wavelet@math.scarolina.edu.


A 5 minute course in wavelet transforms, by Richard Kirk <rak@crosfield.co.uk>:

Do you know what a Haar transform is? Its a transform to another orthonormal
space (like the DFT), but the basis functions are a set of square wave bursts
like this...

   +--+                         +------+
   +  |  +------------------    +      |      +--------------
      +--+                             +------+

         +--+                                 +------+
   ------+  |  +------------    --------------+      |      +
            +--+                                     +------+

               +--+             +-------------+
   ------------+  |  +------    +             |             +
                  +--+                        +-------------+

                     +--+       +---------------------------+
   ------------------+  |  +    +                           +
                        +--+

This is the set of functions for an 8-element 1-D Haar transform. You
can probably see how to extend this to higher orders and higher dimensions
yourself. This is dead easy to calculate, but it is not what is usually
understood by a wavelet transform.

If you look at the eight Haar functions you see we have four functions
that code the highest resolution detail, two functions that code the
coarser detail, one function that codes the coarser detail still, and the 
top function that codes the average value for the whole `image'.

Haar function can be used to code images instead of the DFT. With bilevel
images (such as text) the result can look better, and it is quicker to code.
Flattish regions, textures, and soft edges in scanned images get a nasty
`blocking' feel to them. This is obvious on hardcopy, but can be disguised on
color CRTs by the effects of the shadow mask. The DCT gives more consistent
results.

This connects up with another bit of maths sometimes called Multispectral
Image Analysis, sometimes called Image Pyramids.

Suppose you want to produce a discretely sampled image from a continuous 
function. You would do this by effectively `scanning' the function using a
sinc function [ sin(x)/x ] `aperture'. This was proved by Shannon in the 
`forties. You can do the same thing starting with a high resolution
discretely sampled image. You can then get a whole set of images showing 
the edges at different resolutions by differencing the image at one
resolution with another version at another resolution. If you have made this
set of images properly they ought to all add together to give the original 
image.

This is an expansion of data. Suppose you started off with a 1K*1K image.
You now may have a 64*64 low resolution image plus difference images at 128*128
256*256, 512*512 and 1K*1K. 

Where has this extra data come from? If you look at the difference images you 
will see there is obviously some redundancy as most of the values are near 
zero. From the way we constructed the levels we know that locally the average
must approach zero in all levels but the top. We could then construct a set of
functions out of the sync functions at any level so that their total value 
at all higher levels is zero. This gives us an orthonormal set of basis 
functions for a transform. The transform resembles the Haar transform a bit,
but has symmetric wave pulses that decay away continuously in either direction
rather than square waves that cut off sharply. This transform is the
wavelet transform ( got to the point at last!! ).

These wavelet functions have been likened to the edge detecting functions
believed to be present in the human retina.


Loren I. Petrich <lip@s1.gov> adds that order 2 or 3 Daubechies
discrete wavelet transforms have a speed comparable to DCT's, and
usually achieve compression a factor of 2 better for the same image
quality than the JPEG 8*8 DCT. (See item 25 in part 1 of this FAQ for
references on fast DCT algorithms.)

------------------------------------------------------------------------------

Subject: [73] What is the theoretical compression limit?


There is no compressor that is guaranteed to compress all possible input
files. If it compresses some files, then it must enlarge some others.
This can be proven by a simple counting argument (see question 9).

As an extreme example, the following algorithm achieves 100%
compression for one special input file and enlarges all other files by
only one bit:

- if the input data is <insert your favorite one here>, output an empty file.
- otherwise output one bit (zero or one) followed by the input data.

The concept of theoretical compression limit is meaningful only
if you have a model for your input data. See question 70 above
for some examples of data models.

------------------------------------------------------------------------------

Subject: [74] Introduction to JBIG


A short introduction, written by Mark Adler <madler@cco.caltech.edu>:

  JBIG losslessly compresses binary (one-bit/pixel) images.  (The B stands
  for bi-level.)  Basically it models the redundancy in the image as the
  correlations of the pixel currently being coded with a set of nearby
  pixels called the template.  An example template might be the two
  pixels preceding this one on the same line, and the five pixels centered
  above this pixel on the previous line.  Note that this choice only
  involves pixels that have already been seen from a scanner.

  The current pixel is then arithmetically coded based on the eight-bit
  (including the pixel being coded) state so formed.  So there are (in this
  case) 256 contexts to be coded.  The arithmetic coder and probability
  estimator for the contexts are actually IBM's (patented) Q-coder.  The
  Q-coder uses low precision, rapidly adaptable (those two are related)
  probability estimation combined with a multiply-less arithmetic coder.
  The probability estimation is intimately tied to the interval calculations
  necessary for the arithmetic coding.

  JBIG actually goes beyond this and has adaptive templates, and probably
  some other bells and whistles I don't know about.  You can find a
  description of the Q-coder as well as the ancestor of JBIG in the Nov 88
  issue of the IBM Journal of Research and Development.  This is a very
  complete and well written set of five articles that describe the Q-coder
  and a bi-level image coder that uses the Q-coder.

  You can use JBIG on grey-scale or even color images by simply applying
  the algorithm one bit-plane at a time.  You would want to recode the
  grey or color levels first though, so that adjacent levels differ in
  only one bit (called Gray-coding).  I hear that this works well up to
  about six bits per pixel, beyond which JPEG's lossless mode works better.
  You need to use the Q-coder with JPEG also to get this performance.

  Actually no lossless mode works well beyond six bits per pixel, since
  those low bits tend to be noise, which doesn't compress at all.

  Anyway, the intent of JBIG is to replace the current, less effective
  group 3 and 4 fax algorithms.


Another introduction to JBIG, written by Hank van Bekkem <jbek@oce.nl>:

  The following description of the JBIG algorithm is derived from
  experiences with a software implementation I wrote following the
  specifications in the revision 4.1 draft of September 16, 1991. The
  source will not be made available in the public domain, as parts of
  JBIG are patented.

  JBIG (Joint Bi-level Image Experts Group) is an experts group of ISO,
  IEC and CCITT (JTC1/SC2/WG9 and SGVIII). Its job is to define a
  compression standard for lossless image coding ([1]). The main
  characteristics of the proposed algorithm are:
  - Compatible progressive/sequential coding. This means that a
    progressively coded image can be decoded sequentially, and the
    other way around.
  - JBIG will be a lossless image compression standard: all bits in
    your images before and after compression and decompression will be
    exactly the same.

  In the rest of this text I will first describe the JBIG algorithm in
  a short abstract of the draft. I will conclude by saying something
  about the value of JBIG.


  JBIG algorithm.
  --------------

  JBIG parameter P specifies the number of bits per pixel in the image.
  Its allowable range is 1 through 255, but starting at P=8 or so,
  compression will be more efficient using other algorithms. On the
  other hand, medical images such as chest X-rays are often stored with
  12 bits per pixel, while no distorsion is allowed, so JBIG can
  certainly be of use in this area. To limit the number of bit changes
  between adjacent decimal values (e.g. 127 and 128), it is wise to use
  Gray coding before compressing multi-level images with JBIG. JBIG
  then compresses the image on a bitplane basis, so the rest of this
  text assumes bi-level pixels.

  Progressive coding is a way to send an image gradually to a receiver
  instead of all at once. During sending, more detail is sent, and the
  receiver can build the image from low to high detail. JBIG uses
  discrete steps of detail by successively doubling the resolution. The
  sender computes a number of resolution layers D, and transmits these
  starting at the lowest resolution Dl. 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 (if P>1) are intermixed in the coded data.
  A progressive 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. Reference [2]
  contains a full description, I will only outline the basic principles
  here.

  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 assigns, just
  like a Huffman coder, more bits to less probable symbols, and so
  achieves compression. The Q-coder can, unlike a Huffmann coder,
  assign one output codebit 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.

  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 value.
  ----------

  In my opinion, JBIG can be regarded as two combined devices:
  - Providing the user the service of sending or storing multiple
    representations of images at different resolutions without any
    extra cost in storage. 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.
  - Providing the user 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 is however, just like Group 4, somewhat
    sensitive to noise in images. This means that the compression ratio
    decreases when the amount of noise in your images increases.

  An example of an application would be browsing through an image
  database, e.g. an EDMS (engineering document management system).
  Large A0 size drawings at 300 dpi or so would be stored using five
  resolution layers. The lowest resolution layer would fit on a
  computer screen. Base layer compressed data would be stored at the
  beginning of the compressed file, thus making browsing through large
  numbers of compressed drawings possible by reading and decompressing
  just the first small part of all files. When the user stops browsing,
  the system could automatically start decompressing all remaining
  detail for printing at high resolution.

  [1] "Progressive Bi-level Image Compression, Revision 4.1", ISO/IEC
      JTC1/SC2/WG9, CD 11544, September 16, 1991
  [2] "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 (See also the other
      articles about the Q-coder in this issue)
Xref: sparky comp.compression:4539 comp.os.msdos.4dos:43