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# ZPCodec.h

//C-  -*- C++ -*-
//C- -------------------------------------------------------------------
//C- DjVuLibre-3.5
//C- Copyright (c) 2002  Leon Bottou and Yann Le Cun.
//C-
//C- This software is subject to, and may be distributed under, the
//C- accompanied the software or you may obtain a copy of the license
//C- from the Free Software Foundation at http://www.fsf.org .
//C-
//C- This program is distributed in the hope that it will be useful,
//C- but WITHOUT ANY WARRANTY; without even the implied warranty of
//C- MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
//C- GNU General Public License for more details.
//C-
//C- DjVuLibre-3.5 is derived from the DjVu(r) Reference Library
//C- distributed by Lizardtech Software.  On July 19th 2002, Lizardtech
//C- Software authorized us to replace the original DjVu(r) Reference
//C- Library notice by the following text (see doc/lizard2002.djvu):
//C-
//C-  ------------------------------------------------------------------
//C- | DjVu (r) Reference Library (v. 3.5)
//C- | The DjVu Reference Library is protected by U.S. Pat. No.
//C- | 6,058,214 and patents pending.
//C- |
//C- | This software is subject to, and may be distributed under, the
//C- | GNU General Public License, Version 2. The license should have
//C- | accompanied the software or you may obtain a copy of the license
//C- | from the Free Software Foundation at http://www.fsf.org .
//C- |
//C- | The computer code originally released by LizardTech under this
//C- | license and unmodified by other parties is deemed "the LIZARDTECH
//C- | ORIGINAL CODE."  Subject to any third party intellectual property
//C- | claims, LizardTech grants recipient a worldwide, royalty-free,
//C- | non-exclusive license to make, use, sell, or otherwise dispose of
//C- | the LIZARDTECH ORIGINAL CODE or of programs derived from the
//C- | LIZARDTECH ORIGINAL CODE in compliance with the terms of the GNU
//C- | General Public License.   This grant only confers the right to
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//C- | the extent such infringement is reasonably necessary to enable
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//C- | The LIZARDTECH ORIGINAL CODE is provided "AS IS" WITHOUT WARRANTY
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//C- | TO ANY WARRANTY OF NON-INFRINGEMENT, OR ANY IMPLIED WARRANTY OF
//C- | MERCHANTABILITY OR FITNESS FOR A PARTICULAR PURPOSE.
//C- +------------------------------------------------------------------
//
// $Id: ZPCodec.h,v 1.9 2003/11/07 22:08:22 leonb Exp$
// $Name: release_3_5_17$

#ifndef _ZPCODEC_H
#define _ZPCODEC_H
#ifdef HAVE_CONFIG_H
#include "config.h"
#endif
#if NEED_GNUG_PRAGMAS
# pragma interface
#endif

// From: Leon Bottou, 1/31/2002
// Almost equal to my initial code.

#include "GContainer.h"

#ifdef HAVE_NAMESPACES
namespace DJVU {
# ifdef NOT_DEFINED // Just to fool emacs c++ mode
}
#endif
#endif

class ByteStream;

/** @name ZPCodec.h

Files #"ZPCodec.h"# and #"ZPCodec.cpp"# implement a fast binary adaptive
quasi-arithmetic coder named ZP-Coder.  Because of its speed and
convenience, the ZP-Coder is used in several parts of the DjVu reference
library (See \Ref{BSByteStream.h}, \Ref{JB2Image.h}, \Ref{IW44Image.h}).
The following comments avoid the theory (see the historical remarks for
useful pointers) and concentrate on the user perspective on the ZP-Coder.

{\bf Introduction} ---
Encoding consists of transforming a sequence of {\em message bits} into a
sequence of {\em code bits}. Decoding consists of retrieving the message
bits using only the code bits.  We can make the code smaller than the
message as soon as we can predict a message bit on the basis of a {\em
coding context} composed of previously encoded or decoded bits. If the
prediction is always correct, we do not even need to encode the message
bit. If the prediction is totally unreliable, we need to generate one code
bit in order to unambiguously specify the message bit.  In other words,
the more reliable the prediction, the more compression we get.

The ZP-Coder handles prediction by means of {\em context variables} (see
\Ref{BitContext}).  There must be a context variable for each possible
combination of context bits.  Both the encoder and the decoder use same
context variable for coding each message bit.  For instance, we can code a
binary image by successively coding all the pixels (the message bits) in
row and column order.  It is reasonable to assume that each pixel can be
reasonably well predicted by looking at a few (say 10) neighboring pixels
located above and to the left of the current pixel.  Since these 10 pixels
make 1024 combinations, we need 1024 context variables. Each pixel is
encoded using the context variable corresponding to the values of the 10
neighboring pixels.  Each pixel will be decoded by specifying the same
context variable corresponding to the values of these 10 pixels. This is
possible because these 10 pixels (located above and to the left) have
already been decoded and therefore are known by the decoder program.

The context variables are initially set to zero, which mean that we do not
know yet how to predict the current message bit on the basis of the
context bits. While coding the message bits, the ZP-Coder automatically
estimates the frequencies of #0#s and #1#s coded using each context
variable.  These frequencies actually provide a prediction (the most
probable bit value) and an estimation of the prediction reliability (how
often the prediction was correct in the past).  All this statistical
information is stored into the context variable after coding each bit.  In
other words, the more we code bits within a particular context, the better
the ZP-Coder adapts its prediction model, and the more compression we can
obtain.

All this adaptation works indeed because both the encoder program and the
decoder program are always synchronized. Both the encoder and the decoder
see the same message bits encoded (or decoded) with the same context
variables.  Both the encoder and the decoder apply the same rules to
update the context variables and improve the predictors.  Both the encoder
and the decoder programs use the same predictors for any given message
bit.  The decoder could not work if this was not the case.

Just before encoding a message bit, all the context variables in the
encoder program contain certain values. Just before decoding this message
bit, all the context variables in the decoder program must contain the same
values as for the encoder program.  This is guaranteed as long as
each prediction only depends on already coded bits: {\em the coding context,
on which the each prediction is based, must be composed of message bits which

{\bf Usage} ---
Once you know how to organize the predictions (i.e. which coding context
to use, how many context variables to initialize, etc.), using the
ZP-Coder is straightforward (see \Ref{ZPCodec Examples}):
\begin{itemize}
\item The {\em encoder program} allocates context variables and
initializes them to zero. It then constructs a \Ref{ZPCodec} object for
encoding. For each message bit, the encoder program retrieves the context
bits, selects a context variable on the basis of the context bits and
calls member function \Ref{ZPCodec::encoder} with the message bit and a
reference to the context variable.
\item The {\em decoder program} allocates context variables and
initializes them to zero. It then constructs a \Ref{ZPCodec} object for
decoding. For each message bit, the decoder program retrieves the context
bits, selects a context variable on the basis of the context bits and
calls member function \Ref{ZPCodec::decoder} with a reference to the
context variable. This function returns the message bit.
\end{itemize}
Functions #encoder# and #decoder# only require a few machine cycles to
perform two essential tasks, namely {\em coding} and {\em context
adaptation}.  Function #decoder# often returns after two arithmetic
operations only.  To make your program fast, you just need to feed message
bits and context variables fast enough.

{\bf History} --- The ZP-Coder is similar in function and performance to
the seminal Q-Coder (Pennebaker, Mitchell, Langdon, Arps, IBM J. Res
Dev. 32, 1988). An improved version of the Q-Coder, named QM-Coder, has
been described in certain parts of the JPEG standard.  Unfortunate patent
policies have made these coders very difficult to use in general purpose
applications.  The Z-Coder is constructed using a new approach based on an
extension of the Golomb codes (Bottou, Howard, Bengio, IEEE DCC 98, 1998
\URL[DjVu]{http://www.research.att.com/~leonb/DJVU/bottou-howard-bengio/}
\URL[PostScript]{http://www.research.att.com/~leonb/PS/bottou-howard-bengio.ps.gz})
This new approach does not infringe the QM-Coder patents.  Unfortunately
the Z-Coder is dangerously close to the patented Arithmetic MEL Coder.
Therefore we wrote the ZP-Coder (pronounce Zee-Prime Coder) which we
believe is clear of legal problems.  Needless to say, AT&T has patents
pending for both the Z-Coder and the ZP-Coder, licenced to LizardTech.
The good news however is that we can grant a license to use the ZP-Coder
in free software'' without further complication. See the Copyright

@memo
@version
#$Id: ZPCodec.h,v 1.9 2003/11/07 22:08:22 leonb Exp$#
@author
L\'eon Bottou <leonb@research.att.com> */
//@{

/** Context variable.
Variables of type #BitContext# hold a single byte describing how to encode
or decode message bits with similar statistical properties.  This single
byte simultaneously represents the current estimate of the bit probability
distribution (which is determined by the frequencies of #1#s and #0#s
already coded with this context) and the confidence in this estimate
(which determines how fast the estimate can change.)

A coding program typically allocates hundreds of context variables.  Each
coding context is initialized to zero before encoding or decoding.  Value
zero represents equal probabilities for #1#s and #0#s with a minimal
confidence and therefore a maximum adaptation speed.  Each message bit is
encoded using a coding context determined as a function of previously
encoded message bits.  The decoder therefore can examine the previously
decoded message bits and decode the current bit using the same context as
the encoder.  This is critical for proper decoding.
*/
typedef unsigned char  BitContext;

/** Performs ZP-Coder encoding and decoding.  A ZPCodec object must either
constructed for encoding or for decoding.  The ZPCodec object is connected
with a \Ref{ByteStream} object specified at construction time.  A ZPCodec
object constructed for decoding reads code bits from the ByteStream and
returns a message bit whenever function \Ref{decoder} is called.  A
ZPCodec constructed for encoding processes the message bits provided by
function \Ref{encoder} and writes the corresponding code bits to
ByteStream #bs#.

You should never directly access a ByteStream object connected to a valid
ZPCodec object. The most direct way to access the ByteStream object
consists of using the "pass-thru" versions of functions \Ref{encoder} and
\Ref{decoder}.

The ByteStream object can be accessed again after the destruction of the
ZPCodec object.  Note that the encoder always flushes its internal buffers
and writes a few final code bytes when the ZPCodec object is destroyed.
Note also that the decoder often reads a few bytes beyond the last code byte
written by the encoder.  This lag means that you must reposition the
ByteStream after the destruction of the ZPCodec object and before re-using
the ByteStream object (see \Ref{IFFByteStream}.)

Please note also that the decoder has no way to reliably indicate the end
of the message bit sequence.  The content of the message must be designed
in a way which indicates when to stop decoding.  Simple ways to achieve
this consists of announcing the message length at the beginning (like a
pascal style string), or of defining a termination code (like a null
terminated string).  */

00245 class ZPCodec : public GPEnabled {
protected:
ZPCodec (GP<ByteStream> gbs, const bool encoding, const bool djvucompat=false);
public:
class Encode;
class Decode;

/// Non-virtual destructor.
~ZPCodec();
/** Constructs a ZP-Coder.  If argument #encoding# is zero, the ZP-Coder
object will read code bits from the ByteStream #bs# and return a message
bit whenever function #decoder# is called.  If flag #encoding# is set
the ZP-Coder object will process the message bits provided by function
#encoder# and write code bits to ByteStream #bs#.  Optional flag
#djvucompat# selects a slightly less efficient adaptation table which is
used by the DjVu project.  This is required in order to ensure the
bitstream compatibility.  You should not use this flag unless you want
to decode JB2, IW44 or BZZ encoded data. */
static GP<ZPCodec> create(
GP<ByteStream> gbs, const bool encoding, const bool djvucompat=false);

/** Encodes bit #bit# using context variable #ctx#.  Argument #bit# must be
#0# or #1#. This function should only be used with ZP-Coder objects
created for encoding. It may modify the contents of variable #ctx# in
order to perform context adaptation. */
void encoder(int bit, BitContext &ctx);

/** Decodes a bit using context variable #ctx#. This function should only be
used with ZP-Coder objects created for decoding. It may modify the
contents of variable #ctx# in order to perform context adaptation. */
int  decoder(BitContext &ctx);

/** Encodes bit #bit# without compression (pass-thru encoder).  Argument
#bit# must be #0# or #1#. No compression will be applied. Calling this
function always increases the length of the code bit sequence by one
bit. */
void encoder(int bit);

/** Decodes a bit without compression (pass-thru decoder).  This function
retrieves bits encoded with the pass-thru encoder. */
int  decoder(void);
#ifdef ZPCODEC_BITCOUNT
/** Counter for code bits (requires #-DZPCODEC_BITCOUNT#). This member
variable is available when the ZP-Coder is compiled with option
#-DZPCODEC_BITCOUNT#.  Variable #bitcount# counts the number of code
bits processed by the coder since the construction of the object.  This
variable can be used to evaluate how many code bits are spent on various
components of the message. */
int bitcount;
#endif
struct Table {
unsigned short p;
unsigned short m;
BitContext     up;
BitContext     dn;
};
void newtable(ZPCodec::Table *table);
BitContext state(float prob1);
void encoder_nolearn(int pix, BitContext &ctx);
int  decoder_nolearn(BitContext &ctx);
inline int  IWdecoder(void);
inline void IWencoder(const bool bit);
protected:
// coder status
GP<ByteStream> gbs;           // Where the data goes/comes from
ByteStream *bs;               // Where the data goes/comes from
const bool encoding;          // Direction (0=decoding, 1=encoding)
unsigned char byte;
unsigned char scount;
unsigned char delay;
unsigned int  a;
unsigned int  code;
unsigned int  fence;
unsigned int  subend;
unsigned int  buffer;
unsigned int  nrun;
// table
unsigned int  p[256];
unsigned int  m[256];
BitContext    up[256];
BitContext    dn[256];
// machine independent ffz
char          ffzt[256];
// encoder private
void einit (void);
void eflush (void);
void outbit(int bit);
void zemit(int b);
void encode_mps(BitContext &ctx, unsigned int z);
void encode_lps(BitContext &ctx, unsigned int z);
void encode_mps_simple(unsigned int z);
void encode_lps_simple(unsigned int z);
void encode_mps_nolearn(unsigned int z);
void encode_lps_nolearn(unsigned int z);
// decoder private
void dinit(void);
int  ffz(unsigned int x);
int  decode_sub(BitContext &ctx, unsigned int z);
int  decode_sub_simple(int mps, unsigned int z);
int  decode_sub_nolearn(int mps, unsigned int z);
private:
// no copy allowed (hate c++)
ZPCodec(const ZPCodec&);
ZPCodec& operator=(const ZPCodec&);
#ifdef ZPCODEC_FRIEND
friend ZPCODEC_FRIEND;
#endif
};

// INLINE CODE

inline void
00365 ZPCodec::encoder(int bit, BitContext &ctx)
{
unsigned int z = a + p[ctx];
if (bit != (ctx & 1))
{
encode_lps(ctx, z);
}else if (z >= 0x8000)
{
encode_mps(ctx, z);
}else
{
a = z;
}
}

inline int
ZPCodec::IWdecoder(void)
{
return decode_sub_simple(0,0x8000 + ((a+a+a) >> 3));
}

inline int
00387 ZPCodec::decoder(BitContext &ctx)
{
unsigned int z = a + p[ctx];
if (z <= fence)
{ a = z; return (ctx&1); }
return decode_sub(ctx, z);
}

inline void
ZPCodec::encoder_nolearn(int bit, BitContext &ctx)
{
unsigned int z = a + p[ctx];
if (bit != (ctx & 1))
encode_lps_nolearn(z);
else if (z >= 0x8000)
encode_mps_nolearn(z);
else
a = z;
}

inline int
ZPCodec::decoder_nolearn(BitContext &ctx)
{
unsigned int z = a + p[ctx];
if (z <= fence)
{ a = z; return (ctx&1); }
return decode_sub_nolearn( (ctx&1), z);
}

inline void
00417 ZPCodec::encoder(int bit)
{
if (bit)
encode_lps_simple(0x8000 + (a>>1));
else
encode_mps_simple(0x8000 + (a>>1));
}

inline int
00426 ZPCodec::decoder(void)
{
return decode_sub_simple(0, 0x8000 + (a>>1));
}

inline void
ZPCodec::IWencoder(const bool bit)
{
const int z = 0x8000 + ((a+a+a) >> 3);
if (bit)
{
encode_lps_simple(z);
}else
{
encode_mps_simple(z);
}
}

/** @name ZPCodec Examples

Binary adaptive coders are efficient and very flexible.  Unfortunate
intellectual property issues however have limited their popularity.  As a
consequence, few programmers have a direct experience of using such a
coding device.  The few examples provided in this section demonstrate how
we think the ZP-Coder should be used.

{\bf Encoding Multivalued Symbols} ---
Since the ZP-Coder is a strictly binary coder, every message must be
reduced to a sequence of bits (#0#s or #1#s).  It is often convenient to
consider that a message is a sequence of symbols taking more than two
values.  For instance, a character string may be a sequence of bytes, and
each byte can take 256 values.  Each byte of course is composed of eight
bits that we can encode in sequence.  The real issue however consists of
deciding how we will use context variables in order to let the ZP-Coder
learn the probability distribution of the byte values.

The most significant bit #b0# decides whether the byte is in range 0..127
or in range 128..255.  We let the ZP-Coder learn how to predict this bit
by allocating one context variable for it.  The second most significant
byte #b1# has two distinct meanings depending of bit #b0#.  If bit #b0# is
#0#, bit #b1# decides whether the byte is in range 0..63 or 64..127.  If
bit #b0# is #1#, bit #b1# decides whether the byte is in range 128..191 or
192..255.  The prediction for bit #b1# must therefore depend on the value
of #b0#.  This is why we will allocate two context variables for this bit.
If bit #b0# is #0#, we will use the first variable; if bit #b0# is #1#, we
will use the second variable.  The next bit #b2# has four meanings and
therefore we will use four context variables, etc.  This analysis leads to
a total of #1+2+4+...+128# = #255# context variables for encoding one
byte.  This encoding procedure can be understood as a binary decision
tree with a dedicated context variable for predicting each decision.
\begin{verbatim}
[>=128]----n---[>=64?]----n----[>31?]  ...
\              ---y----[>95?]  ...
\
--y---[>=192?]----n---[>=160?] ...
---y---[>=224?] ...
\end{verbatim}
The following decoding function illustrates a very compact way to
implement such a decision tree.  Argument #ctx# points to an array of 255
#BitContext# variables.  Macro #REPEAT8# is a shorthand notation for eight
repetitions of its argument.
\begin{verbatim}
int decode_8_bits(ZPCodec &zp, BitContext *ctx )
{
int n = 1;
REPEAT8( { n = (n<<1) | (zp.decoder(ctx[n-1])); } );
return n & 0xff;
}
\end{verbatim}
The binary representation of variable #n# is always composed of a #1#
followed by whichever bits have been decoded so far. This extra bit #1# in
fact is a nice trick to flatten out the tree structure and directly
address the array of context variables.  Bit #b0# is decoded using the
first context variable since #n# is initially #1#.  Bit #b1# is decoded
using one of the next two variables in the array, since #n# is either #2#
(#10# in binary) or #3# (#11# in binary).  Bit #b2# will be decoded using
one of the next four variables, since #n# ranges from #4# (#100# in
binary) to #7# (#111# in binary).  The final result is given by removing
the extra #1# in variable #n#.

The corresponding encoding function is almost as compact. Argument #ctx#
again is an array of 255 #BitContext# variables.  Each bit of byte #x# is
encoded and shifted into variable #n# as in the decoding function.
Variable #x# in fact contains the bits to be encoded. Variable #n#
contains a #1# followed by the already encoded bits.
\begin{verbatim}
void encode_8_bits(ZPCodec &zp, int x, BitContext *ctx )
{
int n = 1;
REPEAT8( { int b=((x&0x80)?1:0);  x=(x<<1);
zp.encoder(b,ctx[n-1]);  n=(n<<1)|(b); } );
}
\end{verbatim}
The ZP-Coder automatically adjusts the content of the context variables
while coding (recall the context variable argument is passed to functions
#encoder# and #decoder# by reference).  The whole array of 255 context
variables can be understood as a "byte context variable".  The estimated
probability of each byte value is indeed the product of the estimated
probabilities of the eight binary decisions that lead to that value in the
decision tree.  All these probabilities are adapted by the underlying

{\bf Application} ---
We consider now a simple applications consisting of encoding the
horizontal and vertical coordinates of a cloud of points. Each coordinate
requires one byte.  The following function illustrates a possible
implementation:
\begin{verbatim}
void encode_points(const char *filename, int n, int *x, int *y)
{
StdioByteStream bs(filename, "wb");
bs.write32(n);             // Write number of points.
ZPCodec zp(bs, 1);         // Construct encoder and context vars.
BitContext ctxX[255], ctxY[255];
memset(ctxX, 0, sizeof(ctxX));
memset(ctxY, 0, sizeof(ctxY));
for (int i=0; i<n; i++) {  // Encode coordinates.
encode_8_bits(zp, x[i], ctxX);
encode_8_bits(zp, y[i], ctxY);
}
}
\end{verbatim}
The decoding function is very similar to the encoding function:
\begin{verbatim}
int decode_points(const char *filename, int *x, int *y)
{
StdioByteStream bs(filename,"rb");
ZPCodec zp(bs, 0);        // Construct decoder and context vars.
BitContext ctxX[255], ctxY[255];
memset(ctxX, 0, sizeof(ctxX));
memset(ctxY, 0, sizeof(ctxY));
for (int i=0; i<n; i++) { // Decode coordinates.
x[i] = decode_8_bits(zp, ctxX);
y[i] = decode_8_bits(zp, ctxY);
}
return n;                 // Return number of points.
}
\end{verbatim}
The ZP-Coder automatically estimates the probability distributions of both
the horizontal and vertical coordinates. These estimates are used to
efficiently encode the point coordinates.  This particular implementation
is a good option if we assume that the order of the points is significant
and that successive points are independent.  It would be much smarter
otherwise to sort the points and encode relative displacements between
successive points.

{\bf Huffman Coding Tricks} ---
Programmers with experience in Huffman codes can see the similarity in the
ZP-Coder.  Huffman codes also organize the symbol values as a decision
tree. The tree is balanced in such a way that each decision is as
unpredictable as possible (i.e. both branches must be equally probable).
This is very close to the ZP-Coder technique described above.  Since we
allocate one context variable for each decision, our tree need not be
balanced: the context variable will track the decision statistics and the
ZP-Coder will compensate optimally.

There are good reasons however to avoid unbalanced trees with the ZP-Coder.
Frequent symbol values may be located quite deep in a poorly balanced
tree.  This increases the average number of message bits (the number of
decisions) required to code a symbol.  The ZP-Coder will be called more
often, making the coding program slower.  Furthermore, each message
bit is encoded using an estimated distribution.  All these useless message
bits mean that the ZP-Coder has more distributions to adapt.  This
extra adaptation work will probably increase the file size.

Huffman codes are very fast when the tree structure is fixed beforehand.
Such {\em static Huffman codes} are unfortunately not very efficient
because the tree never matches the actual data distribution.  This is why
such programs almost always define a data dependent tree structure.  This
structure must then be encoded in the file since the decoder must know it
before decoding the symbols.  Static Huffman codes however become very
efficient when decisions are encoded with the ZP-Coder.  The tree
structure represents a priori knowledge about the distribution of the
symbol values.  Small data discrepancies will be addressed transparently
by the ZP-Coder.

{\bf Encoding Numbers} ---
This technique is illustrated with the following number encoding example.
The multivalued technique described above is not practical with large
numbers because the decision tree has too many nodes and requires too many
context variables.  This problem can be solved by using a priori knowledge
about the probability distribution of our numbers.

Assume for instance that the distribution is symmetrical and that small
numbers are much more probable than large numbers.  We will first group
our numbers into several sets.  Each number is coded by first coding which
set contains the number and then coding a position within the set.  Each
set contains #2^n# numbers that we consider roughly equiprobable.  Since
the most probable values occur much more often, we want to model their
probability more precisely. Therefore we use small sets for the most
probable values and large sets for the least probable values, as
demonstrated below.
\begin{verbatim}
A---------------- {0}                                 (size=1)
------B---C---- {1}            or {-1}              (size=1)
\   --- {2,3}          or {-2,-3}           (size=2)
D------ {4...131}      or {-4...-131}       (size=128)
----- {132...32899}  or {-132...-32899}   (size=32768)
\end{verbatim}
We then organize a decision tree for coding the set identifier.  This
decision tree is balanced using whatever a priori knowledge we have about
the probability distribution of the number values, just like a static
Huffman tree.  Each decision (except the sign decision) is then coded
using a dedicated context variable.
\begin{verbatim}
if (! zp.decoder(ctx_A)) {             // decision A
return 0;
} else {
if (! zp.decoder(ctx_B)) {          // + decision B
if (! zp.decoder(ctx_C)) {        // ++ decision C
if (! zp.decoder())             // +++ sign decision
return +1;
else
return -1;
} else {
if (! zp.decoder())             // +++ sign decision
return + 2 + zp.decoder();
else
return - 2 - zp.decoder();
}
} else {
if (! zp.decoder(ctx_D)) {        // ++ decision D
if (! zp.decoder())             // +++ sign decision
return + 4 + decode_7_bits(zp);
else
return - 4 - decode_7_bits(zp);
} else {
if (! zp.decoder())             // +++ sign decision
return + 132 + decode_15_bits(zp);
else
return - 132 - decode_15_bits(zp);
}
}
}
\end{verbatim}
Note that the call #zp.decoder()# for coding the sign decision does not use
a context variable.  This is a "pass-thru" variant of \Ref{decoder} which
bypasses the ZP-Coder and just reads a bit from the code sequence.  There
is a corresponding "pass-thru" version of \Ref{encoder} for encoding such
bits.  Similarly, functions #decode_7_bits# and #decode_15_bits# do not
take an array of context variables because, unlike function #decode_8_bits#
listed above, they are based on the pass-thru decoder instead of the
regular decoder.

The ZP-Coder will not learn the probabilities of the numbers within a set
since no context variables have been allocated for that purpose.  This
could be improved by allocating additional context variables for encoding
the position within the smaller sets and using the regular decoding
functions instead of the pass-thru variants.  Only experimentation can tell
what works best for your particular encoding problem.

We have so far explained that the ZP-Coder adaptation algorithm is able to
quickly estimate of the probability distribution of the message bits coded
using a particular context variable.  It is also able to track slow
variations when the actual probabilities change while coding.

Let us consider the cloud of points'' application presented above.
Suppose that we first code points located towards the left side and then
slowly move towards points located on the right side.  The ZP-Coder will
first estimate that the X coordinates are rather on the left side. This
estimation will be progressively revised after seeing more points on the
right side.  Such an ordering of the points obviously violates the point
independence assumption on which our code is based.  Despite our inexact
assumptions, the tracking mechanism allows for better prediction of the X
coordinates and therefore better compression.

However, this is not a perfect solution. The ZP-Coder tracks the changes
because every point seems to be a little bit more on the right side than
suggested by the previous points.  The ZP-Coder coding algorithm is always
slightly misadjusted and we always lose a little on possible compression
ratio.  This is not much of a problem when the probabilities drift slowly.
On the other hand, this can be very significant if the probabilities change
drastically.

Adaptation is always associated with a small loss of efficiency.  The
ZP-Coder updates the probability model whenever it suspects, {\em after
coding}, that the current settings were not optimal.  The model will be
better next time, but a slight loss in compression has occurred.  The
design of ZP-Coder of course minimizes this effect as much as possible.
Yet you will pay a price if you ask too much to the adaptation algorithm.
If you have millions of context variables, it will be difficult to train
them all.  If the probability distributions change drastically while
coding, it will be difficult to track the changes fast enough.

Adaptation on the other hand is a great simplification.  A good data
compression program must (a) represent the data in order to make its
predictability apparent, and (b) perform the predictions and generate the
code bits.  The ZP-Coder is an efficient and effortless solution for

{\bf Practical Debugging Tricks} ---
Sometimes you write an encoding program and a decoding program.
Unfortunately there is a bug: the decoding program decodes half the file
and then just outputs garbage.  There is a simple way to locate the
problem.  In the encoding program, after each call to #encoder#, print the
encoded bit and the value of the context variable.  In the decoding
program, after each call to #decoder#, print the decoded bit and the value
of the context variable.  Both program should print exactly the same thing.
When you find the difference, you find the bug.

@memo Suggestions for efficiently using the ZP-Coder.  */
//@}

// ------------ THE END

#ifdef HAVE_NAMESPACES
}
# ifndef NOT_USING_DJVU_NAMESPACE
using namespace DJVU;
# endif
#endif
#endif



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