Empirical Lossless Compression Bound of a Data Sequence

We consider the lossless compression bound of any individual data sequence. Conceptually, its Kolmogorov complexity is such a bound yet uncomputable. According to Shannon's source coding theorem, the average compression bound is nH, where <i>n</i> is the number of words and <i>H</i> is the entropy of an oracle probability distribution characterizing the data source. The quantity nH(θ^n) obtained by plugging in the maximum likelihood estimate is an underestimate of the bound. Shtarkov showed that the normalized maximum likelihood (NML) distribution is optimal in a minimax sense for any parametric family. Fitting a data sequence-without any <i>a priori</i> distributional assumption-by a relevant exponential family, we apply the local asymptotic normality to show that the NML code length is nH(θ^n)+d2logn2π+log∫Θ|I(θ)|1/2dθ+o(1), where <i>d</i> is dictionary size, |I(θ)| is the determinant of the Fisher information matrix, and Θ is the parameter space. We demonstrate that sequentially predicting the optimal code length for the next word via a Bayesian mechanism leads to the mixture code whose length is given by nH(θ^n)+d2logn2π+log|I(θ^n)|1/2w(θ^n)+o(1), where w(θ) is a prior. The asymptotics apply to not only discrete symbols but also continuous data if the code length for the former is replaced by the description length for the latter. The analytical result is exemplified by calculating compression bounds of protein-encoding DNA sequences under different parsing models. Typically, compression is maximized when parsing aligns with amino acid codons, while pseudo-random sequences remain incompressible, as predicted by Kolmogorov complexity. Notably, the empirical bound becomes more accurate as the dictionary size increases.