Testing various methods for choosing tANS entropy coding automata

Jarek Duda c8eafbaaf6 Merge pull request #1 from aconverse/master 2 years ago
ANStoolkit.cpp 2068e4bf87 Fix precise probability quantization. 3 years ago
LICENSE 658c51f834 Initial commit 4 years ago
README.md ef21af3f77 added link to list of implementations 4 years ago

README.md

AsymmetricNumeralSystemsToolkit

Testing various methods for choosing tANS entropy coding automata

ANS is new approach to entropy coding, which adds fractional bits into consideration into Huffman-like decoder, combining its speed with accuracy of arithmetic coding, like in implementation of Yann Collet. Another advantage in comparison to Huffman coding is that we choose the size of coding tables (L) here, corresponding to 2^depth of Huffman tree, and that there is no need to sort symbol probabilities (linear initialization).

The choice of such finite L state entropy coding automaton consists of 2-3 parts (which generally can be merged):

  • quantization of symbol probability distribution as p[s] ~ q[s]/L fractions (q is a natural number)
  • spreading these symbols in range [0, L-1], such that symbol s appears q[s] times
  • eventual scrambler to simultaneously encrypt encoded message: disturb the found spread in a pseudoranom way accordingly to cryptographic key.

This toolkit contains various choices of these functions, allows to test obtained compression rates, compare with Huffman. Currently it allows to choose betwen:

1) Symbol probability distributions:

  • init_binary(p, n): n binary variables (2^n alphabet size)
  • init_power(rho,m): p[i] is proportional to rho^i
  • init_rand_unif(m): simple random distribution;

2) quantizer (L=2^R):

  • quantize_fast(R): first q[s] = round(p[s]*L), then modify q for the most probable symbol to fulfill nomalization (linear complexity),
  • quantize_prec(R): find the quantization which minimizes sum_s (p[s] - q[s]/L)^2 / p[s] approximation of Kullback_Leibler distance (n log n complexity).

3) symbol spread:

  • spread_range_i(), spread_range_d(), : Huffman coding can be seen as a special case of tANS: if spreading in size 2^k ranges - these spreads take it to general frequencies (in increasing or decreasing order),
  • spread_fast(): basic Yann Collet's random spread - very fast,
  • spread_prec(): very good using only q - wants to distributie symbols in 1/q[s] distance (still linear, but slower),
  • spread_tuned(): uses both q and p - wants to get close to 1/x stationary probability of states (still linear, best compression rate),
  • spread_tuned_s(): uses sort instead of buckets - can be slighlty better, but slower,
  • spread_tuned_p(): uses 1/i approximation of 1/(p*ln(1+1/i)) formula for tuned spread,
  • spread_uABS(): available arithmetic coding/decoding formulas for binary alphabet, e.g. used in Matt Mahoney's fpaqc.

4) scramblers:

  • scrambler0(): for each 2i-1 and 2i positions, with some probability switch symbols - accordingly to PRNG initialized with cryptographic key (the current version assumes at most 256 size alphabet),
  • scrambler1(): takes blocks of four symbols and randomly rotate them cyclically (also 256 size alphabet limit) - faster and stronger disturbtion.

For example for 4 symbol and L=16 states: p=(0.04 0.16 0.16 0.64), q/L=(0.0625 0.1875 0.125 0.625).

<th>method</th><th>symbol spread</th><th>dH/H rate loss</th><th>comment</th>

<th> - </th><th> - </th><th>~0.011</th><th>penalty of quantizer itself</th>

<th> Huffman </th><th> 0011222233333333 </th><th>~0.080</th><th>would give Huffman decoder</th>

<th>spread_range_i()</th><th>0111223333333333</th><th>~0.059</th><th> analogous to Huffman </th>

<th>spread_range_d()</th><th>3333333333221110</th><th>~0.022</th><th> decreasing order </th>

<th>spread_fast()</th><th>0233233133133133</th><th>~0.020</th><th> fast </th>

<th>spread_prec()</th><th>3313233103332133</th><th>~0.015</th><th>generally close to quantization dH/H</th>

<tr>
<th>spread_tuned()</th><th>3233321333313310</th><th>~0.0046</th><th>better than quantization dH/H due to using also p</th>

<th>spread_tuned_s()</th><th>2333312333313310</th><th>~0.0040</th><th>L log L complexity (sort)</th>

<th>spread_tuned_p()</th><th>2331233330133331</th><th>~0.0058</th><th>testing 1/(p ln(1+1/i)) ~ i/p approximation</th>

Tuning shifts symbols right when q[s]/L > p[s] and left otherwise, getting better agreement and so compression rate.

Some sources: article, slides, discussion, list of implementations of ANS.

Feel free to add new probability distributions, better quantizers and spreads.

Jarek Duda, July 2014