Geometric Huffman Coding

Dyadic PMFs can be generated by parsing a stream of independent equiprobable data bits by a full prefix-free code. Formally,

is dyadic if $p_i=2^{-ell_i}, ell_iinmathbb{N}$ and $sum_i 2^{-ell_i}=1$ . Such a PMF

is generated by a prefix-free code with codeword lengths ell_i

. To match dyadic PMFs to communication channels, the Kullback-Leibler distance between the dyadic PMF and the capacity achieving PMF of the considered channel has to be minimized. See Matching Dyadic Distributions to Channels for details. On this web site I provide implementations of algorithms that find the optimal dyadic PMF.

References

Downloads

matching.tar — all implementations from this web site. They are tested in Matlab R2010b and Octave 3.4.0.

Geometric Huffman Coding (GHC)

Given is a vector

with non-negative entries. If additionally the entries sum up to one, than

is a PMF. The objective is to minimize

subject to

is a dyadic PMF. Geometric Huffman Coding (GHC) finds the dyadic PMF

that minimizes mathrm{D}(pVert x)

. GHC constructs a Huffman tree using the following updating rule. Denote by $x_{m}$ and $x_{m-1}geq x_m$ the two smallest entries of

. Then

For comparison, conventional Huffman coding finds the dyadic PMF

that minimizes mathrm{D}(xVert p)

(minimization is here over the second argument) by using the updating rule

Normalized Geometric Huffman Coding (nGHC)

Given is a target vector

with non-negative entries and a weight vector

with strictly positive entries. The objective is to minimize the Kullback-Leibler distance mathrm{D}(pVert x)

normalized by the average weight w^Tp=sum_i p_iw_i

, i.e., to minimize the fraction

subject to

is a dyadic PMF. Normalized Geometric Huffman Coding (nGHC) iteratively finds the solution. nGHC supersedes the LEC Algorithm stated in Matching Dyadic Distributions to Channels.

Cost Constrained Geometric Huffman Coding (ccGHC)

Given is a target vector

with non-negative entries and a weight vector

with strictly positive entries. The objective is to minimize the Kullback-Leibler distance mathrm{D}(pVert x)

subject to an average cost constraint w^Tpleq E

over all dyadic PMFs p. Cost Constrained Geometric Huffman Coding (ccGHC) iteratively finds the solution.

Miscellaneous m-files

kldiv.m in matching.tar is a Matlab implementation of the Kullback-Leibler distance.

Examples

Example 1

octave:11> addpath /home/georg/matching
octave:12> q = [0.328 0.32 0.22 0.11 0.022]';
octave:13> pghc = ghc(q)
pghc =

   0.50000
   0.25000
   0.12500
   0.12500
   0.00000

octave:14> phc = hc(q)
phc =

   0.25000
   0.25000
   0.25000
   0.12500
   0.12500

octave:15> kldiv(pghc,q)
ans =  0.13619
octave:16> kldiv(phc,q)
ans =  0.19548
octave:17> phc_ = [hc(q(1:4));0]
phc_ =

   0.25000
   0.25000
   0.25000
   0.25000
   0.00000

octave:18> kldiv(phc_,q)
ans =  0.15523
octave:19>

Example 2

example2.m in matching.tar. Comparison of normalized geometric Huffman coding and Huffman coding for the (d,k) = (10,19) constraint.

example2.m

w = (11:20)';
C = fsolve(@(s) sum(exp(-w*s))-1,1);
t = exp(-w*C);
d_nghc = nghc(t,w);
d_hc = hc(t);
R_nghc = -d_nghc'*log(d_nghc)/(w'*d_nghc);
R_hc = -d_hc'*log(d_hc)/(w'*d_hc);
disp(['codeword lengths of nghc: ' num2str(-log2(d_nghc'))]);
disp(['codeword lengths of hc  : ' num2str(-log2(d_hc'))]);
disp(['rate achieved by nghc:' num2str(R_nghc)]);
disp(['rate achieved by hc:  ' num2str(R_hc)]);

octave:7> addpath /home/georg/matching
octave:7> example2
codeword lengths of nghc: 3   3   3   3   3   3   4   4   4   4
codeword lengths of hc  : 2   3   3   3   3   4   4   4   5   5
rate achieved by nghc:0.15273
rate achieved by hc:  0.15265
octave:8>