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Last Modified: Jan 11, 2009
| # Data Compression This page contains a bunch of objects that implement various parts of compression algorithms. They can be put together in different ways to construct many different algorithms. Note that the compress_stream object contains complete compression algorithms. So if you just want to compress some data then you can easily use that object and not bother with the others. In the column to the right you can see benchmark data for each of the compress_stream typedefs. The times measured are the time it takes to compress and then decompress each file. It was run on a 3.0ghz P4. For reference see the Canterbury corpus web site. |
Objects compress_stream conditioning_class entropy_decoder entropy_decoder_model entropy_encoder entropy_encoder_model lz77_buffer lzp_buffer Benchmarks kernel_1a kernel_1b kernel_1c kernel_1da kernel_1db kernel_1ea kernel_1eb kernel_1ec kernel_2a kernel_3a kernel_3b |

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# 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.
`#include <dlib/compress_stream.h>`
Detailed Documentation
C++ Example Programs: compress_stream_ex.cpp, file_to_code_ex.cpp
Implementations:

> 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.

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# 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.
`#include <dlib/conditioning_class.h>`
Detailed Documentation
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 semi-sorted 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 semi-sorted 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.

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# entropy_decoder

This object represents an entropy decoder. E.g. the decoding part of an arithmetic coder.
`#include <dlib/entropy_decoder.h>`
Detailed Documentation
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.

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# 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.
`#include <dlib/entropy_decoder_model.h>`
Detailed Documentation
Implementations:

> entropy_decoder_model_kernel_1:
> This object is implemented using the conditioning_class component. It implements an order-0 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 order-1-0 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 order-0 context and conditioning_class_kernel_4b for its order-1 context.

> entropy_decoder_model_kernel_3:
> This object is implemented using the conditioning_class component. It implements an order-2-1-0 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 order-2.
> 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 order-2.
> 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 order-2.

> 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

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# entropy_encoder

This object represents an entropy encoder. E.g. the encoding part of an arithmetic coder.
`#include <dlib/entropy_encoder.h>`
Detailed Documentation
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.

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# 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.
`#include <dlib/entropy_encoder_model.h>`
Detailed Documentation
Implementations:

> entropy_encoder_model_kernel_1:
> This object is implemented using the conditioning_class component. It implements an order-0 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 order-1-0 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 order-0 context and conditioning_class_kernel_4b for its order-1 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 order-2-1-0 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 order-2.
> 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 order-2.
> 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 order-2.
> 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.

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# lz77_buffer

This object represents a pair of buffers (history and lookahead buffers) used during lz77 style compression.
`#include <dlib/lz77_buffer.h>`
Detailed Documentation
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.

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# lzp_buffer

This object represents some variation on the LZP algorithm described by Charles Bloom in his paper "LZP: a new data compression algorithm"
`#include <dlib/lzp_buffer.h>`
Detailed Documentation
Implementations:

> lzp_buffer_kernel_1:
> This object is implemented using the sliding_buffer and uses an order-3 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 order-5-4-3 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.