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PerfectCompress 6. It achieved the top ranking on the Calgary corpus but not on most other benchmarks.

History[ edit ] The following lists the major enhancements to the PAQ algorithm. In addition, there have been a large number of incremental improvements, which are omitted.

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It used fixed weights and did not include an analog or sparse model. It significantly improved compression by adding a Secondary Symbol Estimation SSE stage between the predictor and encoder. SSE inputs a short context and the current prediction and outputs a new prediction from a table. The table entry is then adjusted to reflect the actual bit value.

PAQ3N, released October 9, added a sparse model. At this point, PAQ was competitive with the best PPM compressors and attracted the attention of the data compression community, which resulted in a large number of incremental improvements through April Berto Destasio tuned the models and adjusted the bit count discounting schedule.

Johan de Bock made improvements to the user interface. David A. Scott made improvements to the arithmetic coder. Fabio Buffoni made speed improvements.

During the period May 20, through July 27,Alexander Ratushnyak released seven versions of PAQAR, which made significant compression improvements by adding many new models, multiple mixers with weights selected by context, adding an SSE stage to each mixer output, and adding a preprocessor to improve the compression of Intel executable files. It achieved the top ranking on the Calgary corpus but not on most other benchmarks.

The most recent was submitted on June 5,consisting of compressed data and program source code totalingbytes. However it lacked x86 and a dictionary, so it did not compress Windows executables and English text files as well as PAsQDa.

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The primary difference from PAQ6 is it uses a neural network to combine models rather than a gradient descent mixer. Another feature is PAQ7's ability to compress embedded jpeg and bitmap images in Excel- Word- and pdf-files.

These were experimental pre-release of anticipated PAQ8.

During the exploration phase, a lever is randomly selected with uniform probability ; during the exploitation phase, the best lever is always selected. Adaptive epsilon-greedy strategy based on value differences VDBE : Similar to the epsilon-decreasing strategy, except that epsilon is reduced on basis of the learning progress instead of manual tuning Tokic,

It fixed several issues in PAQ7 poor compression in some cases. PAQ8A also included model for compressing x86 executables.

PAQ8F was released on February 28, PAQ8F had 3 improvements over PAQ8A: a more memory efficient context model, a new indirect context model to improve compression, and a new user interface to support drag and drop Warianty binarne oaandos.

Windows. Holoborodko, with bug fixes on August 24, September 4, Warianty binarne oaandos. September It added a grayscale image model for PGM files. This version has since been ported to 32 bit Windows for several processors, and 32 and 64 bit Linux.

It includes additional models for binary files. Can be optionally compiled with SSE2 support and for bit Linux. The algorithm Warianty binarne Rene GZ notable performance benefits under bit OS. A new experimental version.