[top]compress_stream
This object is pretty straight forward. It has no state and just
contains the functions compress and decompress.
They do just what their names imply to iostream objects.
C++ Example Programs:
compress_stream_ex.cpp,
file_to_code_ex.cppImplementations:compress_stream_kernel_1:
This implementation is done using the entropy_encoder_model and
entropy_decoder_model objects.
kernel_1a  is a typedef for compress_stream_kernel_1 which uses entropy_decoder_model_kernel_1b and entropy_decoder_model_kernel_1b 
kernel_1b  is a typedef for compress_stream_kernel_1 which uses entropy_decoder_model_kernel_2b and entropy_decoder_model_kernel_2b 
kernel_1c  is a typedef for compress_stream_kernel_1 which uses entropy_decoder_model_kernel_3b and entropy_decoder_model_kernel_3b 
kernel_1da  is a typedef for compress_stream_kernel_1 which uses entropy_decoder_model_kernel_4a and entropy_decoder_model_kernel_4a 
kernel_1db  is a typedef for compress_stream_kernel_1 which uses entropy_decoder_model_kernel_4b and entropy_decoder_model_kernel_4b 
kernel_1ea  is a typedef for compress_stream_kernel_1 which uses entropy_decoder_model_kernel_5a and entropy_decoder_model_kernel_5a 
kernel_1eb  is a typedef for compress_stream_kernel_1 which uses entropy_decoder_model_kernel_5b and entropy_decoder_model_kernel_5b 
kernel_1ec  is a typedef for compress_stream_kernel_1 which uses entropy_decoder_model_kernel_5c and entropy_decoder_model_kernel_5c 
compress_stream_kernel_2:
This implementation is done using the entropy_encoder_model and
entropy_decoder_model objects. It also uses the
lz77_buffer object. It uses the entropy coder models to
encode symbols when there is no match found by the lz77_buffer.
kernel_2a  is a typedef for compress_stream_kernel_2 which uses entropy_encoder_model_kernel_2b, entropy_decoder_model_kernel_2b, and lz77_buffer_kernel_2a. 
compress_stream_kernel_3:
This implementation is done using the lzp_buffer object and
crc32 object. It does not use any sort of entropy coding, instead
a byte aligned output method is used.
kernel_3a  is a typedef for compress_stream_kernel_3 which uses lzp_buffer_kernel_1. 
kernel_3b  is a typedef for compress_stream_kernel_3 which uses lzp_buffer_kernel_2. 
[top]conditioning_class
This object represents a conditioning class used for arithmetic style
compression. It maintains the cumulative counts which are needed
by the entropy_encoder and entropy_decoder objects below.
Implementations:conditioning_class_kernel_1:
This implementation is done using an array to store all the counts and they are summed
whenever the cumulative counts are requested. It's pretty straight forward.
kernel_1a  is a typedef for conditioning_class_kernel_1 
kernel_1a_c 
is a typedef for kernel_1a that checks its preconditions.

conditioning_class_kernel_2:
This implementation is done using a binary tree where each node in the tree represents one symbol and
contains that symbols count and the sum of all the counts for the nodes to the left. This way
when you request a cumulative count it can be computed by visiting log n nodes where n is the
size of the alphabet.
kernel_2a  is a typedef for conditioning_class_kernel_2 
kernel_2a_c 
is a typedef for kernel_2a that checks its preconditions.

conditioning_class_kernel_3:
This implementation is done using an array to store all the counts and they are
summed whenever the cumulative counts are requested. The counts are also kept in
semisorted order to speed up the calculation of the cumulative count.
kernel_3a  is a typedef for conditioning_class_kernel_3 
kernel_3a_c 
is a typedef for kernel_3a that checks its preconditions.

conditioning_class_kernel_4:
This implementation is done using a linked list to store all the counts and they are
summed whenever the cumulative counts are requested. The counts are also kept in
semisorted order to speed up the calculation of the cumulative count. This implementation
also uses the memory_manager component to create a
memory pool of linked list nodes. This implementation is especially useful for high order
contexts and/or very large and sparse alphabets.
kernel_4a  is a typedef for conditioning_class_kernel_4 with a memory pool of 10,000 nodes. 
kernel_4a_c 
is a typedef for kernel_4a that checks its preconditions.

kernel_4b  is a typedef for conditioning_class_kernel_4 with a memory pool of 100,000 nodes. 
kernel_4b_c 
is a typedef for kernel_4b that checks its preconditions.

kernel_4c  is a typedef for conditioning_class_kernel_4 with a memory pool of 1,000,000 nodes. 
kernel_4c_c 
is a typedef for kernel_4c that checks its preconditions.

kernel_4d  is a typedef for conditioning_class_kernel_4 with a memory pool of 10,000,000 nodes. 
kernel_4d_c 
is a typedef for kernel_4d that checks its preconditions.

[top]entropy_decoder
This object represents an entropy decoder. E.g. the decoding part of
an arithmetic coder.
Implementations:entropy_decoder_kernel_1:
This object is implemented using arithmetic coding and is done in the
straight forward way using integers and fixed precision math.
kernel_1a  is a typedef for entropy_decoder_kernel_1 
kernel_1a_c 
is a typedef for kernel_1a that checks its preconditions.

entropy_decoder_kernel_2:
This object is implemented using "range" coding and is done
in the straight forward way using integers and fixed precision math.
kernel_2a  is a typedef for entropy_decoder_kernel_2 
kernel_2a_c 
is a typedef for kernel_2a that checks its preconditions.

[top]entropy_decoder_model
This object represents some kind of statistical model. You
can use it to read symbols from an entropy_decoder and it will calculate
the cumulative counts/probabilities and manage contexts for you.
Implementations:entropy_decoder_model_kernel_1:
This object is implemented using the conditioning_class component.
It implements an order0 finite context model and uses lazy exclusions and update exclusions.
The escape method used is method D.
kernel_1a  is a typedef for entropy_decoder_model_kernel_1 that uses conditioning_class_kernel_1a 
kernel_1b  is a typedef for entropy_decoder_model_kernel_1 that uses conditioning_class_kernel_2a 
kernel_1c  is a typedef for entropy_decoder_model_kernel_1 that uses conditioning_class_kernel_3a 
entropy_decoder_model_kernel_2:
This object is implemented using the conditioning_class component.
It implements an order10 finite context model and uses lazy exclusions and update exclusions.
The escape method used is method D.
kernel_2a  is a typedef for entropy_decoder_model_kernel_2 that uses conditioning_class_kernel_1a 
kernel_2b  is a typedef for entropy_decoder_model_kernel_2 that uses conditioning_class_kernel_2a 
kernel_2c  is a typedef for entropy_decoder_model_kernel_2 that uses conditioning_class_kernel_3a 
kernel_2d  is a typedef for entropy_decoder_model_kernel_2 that uses conditioning_class_kernel_2a for its order0
context and conditioning_class_kernel_4b for its order1 context. 
entropy_decoder_model_kernel_3:
This object is implemented using the conditioning_class component.
It implements an order210 finite context model and uses lazy exclusions and update exclusions.
The escape method used is method D.
kernel_3a  is a typedef for entropy_decoder_model_kernel_3 that uses conditioning_class_kernel_1a for orders 0 and 1
and conditioning_class_kernel_4b for order2. 
kernel_3b  is a typedef for entropy_decoder_model_kernel_3 that uses conditioning_class_kernel_2a for orders 0 and 1
and conditioning_class_kernel_4b for order2. 
kernel_3c  is a typedef for entropy_decoder_model_kernel_3 that uses conditioning_class_kernel_3a for orders 0 and 1
and conditioning_class_kernel_4b for order2. 
entropy_decoder_model_kernel_4:
This object is implemented using a variation of the PPM algorithm described by Alistair Moffat in his paper "Implementing
the PPM data compression scheme."
It provides template arguments to select the maximum order and maximum memory to use. For speed,
exclusions are not used. The escape method used is method D.
kernel_4a  is a typedef for entropy_decoder_model_kernel_4 with the max order set to 4 and the max number
of nodes set to 200,000 
kernel_4b  is a typedef for entropy_decoder_model_kernel_4 with the max order set to 5 and the max number
of nodes set to 1,000,000 
entropy_decoder_model_kernel_5:
This object is implemented using a variation of the PPM algorithm described by Alistair Moffat in his paper "Implementing
the PPM data compression scheme."
It provides template arguments to select the maximum order and maximum memory to use. Exclusions are used. The escape method used is method D.
This implementation is very much like kernel_4 except it is tuned for higher compression rather than speed.
This also uses Dmitry Shkarin's Information Inheritance scheme.
kernel_5a  is a typedef for entropy_decoder_model_kernel_5 with the max order set to 4 and the max number
of nodes set to 200,000 
kernel_5b  is a typedef for entropy_decoder_model_kernel_5 with the max order set to 5 and the max number
of nodes set to 1,000,000 
kernel_5c  is a typedef for entropy_decoder_model_kernel_5 with the max order set to 7 and the max number
of nodes set to 2,500,000 
entropy_decoder_model_kernel_6:
This object just assigns every symbol the same probability. I.e. it uses an order(1) model.
kernel_6a  is a typedef for entropy_decoder_model_kernel_6 
[top]entropy_encoder
This object represents an entropy encoder. E.g. the encoding part of
an arithmetic coder.
Implementations:entropy_encoder_kernel_1:
This object is implemented using arithmetic coding and is done in the
straight forward way using integers and fixed precision math.
kernel_1a  is a typedef for entropy_encoder_kernel_1 
kernel_1a_c 
is a typedef for kernel_1a that checks its preconditions.

entropy_encoder_kernel_2:
This object is implemented using "range" coding and is done
in the straight forward way using integers and fixed precision math.
kernel_2a  is a typedef for entropy_encoder_kernel_2 
kernel_2a_c 
is a typedef for kernel_2a that checks its preconditions.

[top]entropy_encoder_model
This object represents some kind of statistical model. You
can use it to write symbols to an entropy_encoder and it will calculate
the cumulative counts/probabilities and manage contexts for you.
Implementations:entropy_encoder_model_kernel_1:
This object is implemented using the conditioning_class component.
It implements an order0 finite context model and uses lazy exclusions and update exclusions.
The escape method used is method D.
kernel_1a  is a typedef for entropy_encoder_model_kernel_1 that uses conditioning_class_kernel_1a 
kernel_1a_c 
is a typedef for kernel_1a that checks its preconditions.

kernel_1b  is a typedef for entropy_encoder_model_kernel_1 that uses conditioning_class_kernel_2a 
kernel_1b_c 
is a typedef for kernel_1b that checks its preconditions.

kernel_1c  is a typedef for entropy_encoder_model_kernel_1 that uses conditioning_class_kernel_3a 
kernel_1c_c 
is a typedef for kernel_1c that checks its preconditions.

entropy_encoder_model_kernel_2:
This object is implemented using the conditioning_class component.
It implements an order10 finite context model and uses lazy exclusions and update exclusions.
The escape method used is method D.
kernel_2a  is a typedef for entropy_encoder_model_kernel_2 that uses conditioning_class_kernel_1a 
kernel_2a_c 
is a typedef for kernel_2a that checks its preconditions.

kernel_2b  is a typedef for entropy_encoder_model_kernel_2 that uses conditioning_class_kernel_2a 
kernel_2b_c 
is a typedef for kernel_2b that checks its preconditions.

kernel_2c  is a typedef for entropy_encoder_model_kernel_2 that uses conditioning_class_kernel_3a 
kernel_2c_c 
is a typedef for kernel_2c that checks its preconditions.

kernel_2d  is a typedef for entropy_encoder_model_kernel_2 that uses conditioning_class_kernel_2a for its order0
context and conditioning_class_kernel_4b for its order1 context. 
kernel_2d_c 
is a typedef for kernel_2d that checks its preconditions.

entropy_encoder_model_kernel_3:
This object is implemented using the conditioning_class component.
It implements an order210 finite context model and uses lazy exclusions and update exclusions.
The escape method used is method D.
kernel_3a  is a typedef for entropy_encoder_model_kernel_3 that uses conditioning_class_kernel_1a for orders 0 and 1
and conditioning_class_kernel_4b for order2. 
kernel_3a_c 
is a typedef for kernel_3a that checks its preconditions.

kernel_3b  is a typedef for entropy_encoder_model_kernel_3 that uses conditioning_class_kernel_2a for orders 0 and 1
and conditioning_class_kernel_4b for order2. 
kernel_3b_c 
is a typedef for kernel_3b that checks its preconditions.

kernel_3c  is a typedef for entropy_encoder_model_kernel_3 that uses conditioning_class_kernel_3a for orders 0 and 1
and conditioning_class_kernel_4b for order2. 
kernel_3c_c 
is a typedef for kernel_3c that checks its preconditions.

entropy_encoder_model_kernel_4:
This object is implemented using a variation of the PPM algorithm described by Alistair Moffat in his paper "Implementing
the PPM data compression scheme."
It provides template arguments to select the maximum order and maximum memory to use. For speed,
exclusions are not used. The escape method used is method D.
kernel_4a  is a typedef for entropy_encoder_model_kernel_4 with the max order set to 4 and the max number
of nodes set to 200,000 
kernel_4a_c 
is a typedef for kernel_4a that checks its preconditions.

kernel_4b  is a typedef for entropy_encoder_model_kernel_4 with the max order set to 5 and the max number
of nodes set to 1,000,000 
kernel_4b_c 
is a typedef for kernel_4b that checks its preconditions.

entropy_encoder_model_kernel_5:
This object is implemented using a variation of the PPM algorithm described by Alistair Moffat in his paper "Implementing
the PPM data compression scheme."
It provides template arguments to select the maximum order and maximum memory to use. Exclusions are used. The escape method used is method D.
This implementation is very much like kernel_4 except it is tuned for higher compression rather than speed.
This also uses Dmitry Shkarin's Information Inheritance scheme.
kernel_5a  is a typedef for entropy_encoder_model_kernel_5 with the max order set to 4 and the max number
of nodes set to 200,000 
kernel_5a_c 
is a typedef for kernel_5a that checks its preconditions.

kernel_5b  is a typedef for entropy_encoder_model_kernel_5 with the max order set to 5 and the max number
of nodes set to 1,000,000 
kernel_5b_c 
is a typedef for kernel_5b that checks its preconditions.

kernel_5c  is a typedef for entropy_encoder_model_kernel_5 with the max order set to 7 and the max number
of nodes set to 2,500,000 
kernel_5c_c 
is a typedef for kernel_5c that checks its preconditions.

entropy_encoder_model_kernel_6:
This object just assigns every symbol the same probability. I.e. it uses an order(1) model.
kernel_6a  is a typedef for entropy_encoder_model_kernel_6 
kernel_6a_c 
is a typedef for kernel_6a that checks its preconditions.

[top]lz77_buffer
This object represents a pair of buffers (history and lookahead buffers)
used during lz77 style compression.
Implementations:lz77_buffer_kernel_1:
This object is implemented using the sliding_buffer and it
just does simple linear searches of the history buffer to find matches.
kernel_1a  is a typedef for lz77_buffer_kernel_1 that uses sliding_buffer_kernel_1 
kernel_1a_c 
is a typedef for kernel_1a that checks its preconditions.

lz77_buffer_kernel_2:
This object is implemented using the sliding_buffer. It
finds matches by using a hash table.
kernel_2a  is a typedef for lz77_buffer_kernel_2 that uses sliding_buffer_kernel_1 
kernel_2a_c 
is a typedef for kernel_2a that checks its preconditions.

[top]lzp_buffer
This object represents some variation on the LZP algorithm
described by Charles Bloom in his paper "LZP: a new data
compression algorithm"
Implementations:lzp_buffer_kernel_1:
This object is implemented using the sliding_buffer and uses
an order3 model to predict matches.
kernel_1a  is a typedef for lzp_buffer_kernel_1 that uses sliding_buffer_kernel_1 
kernel_1a_c 
is a typedef for kernel_1a that checks its preconditions.

lzp_buffer_kernel_2:
This object is implemented using the sliding_buffer and uses
an order543 model to predict matches.
kernel_2a  is a typedef for lzp_buffer_kernel_2 that uses sliding_buffer_kernel_1 
kernel_2a_c 
is a typedef for kernel_2a that checks its preconditions.
