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M. Oguzhan Kulekci

Publications and source records attributed to M. Oguzhan Kulekci.

2 recordsLinked to original sources

Enumeration of sequences with large alphabets

This study focuses on efficient schemes for enumerative coding of $σ$--ary sequences by mainly borrowing ideas from Öktem & Astola's \cite{Oktem99} hierarchical enumerative coding and Schalkwijk's \cite{Schalkwijk72} asymptotically optimal combinatorial code on binary sequences. By observing that the number of distinct $σ$--dimensional vectors having an inner sum of $n$, where the values in each dimension are in range $[0...n]$ is $K(σ,n) = \sum_{i=0}^{σ-1} {{n-1} \choose {σ-1-i}} {σ \choose {i}}$, we propose representing $C$ vector via enumeration, and present necessary algorithms to perform this task. We prove $\log K(σ,n)$ requires approximately $ (σ-1) \log (σ-1) $ less bits than the naive $(σ-1)\lceil \log (n+1) \rceil$ representation for relatively large $n$, and examine the results for varying alphabet sizes experimentally. We extend the basic scheme for the enumerative coding of $σ$--ary sequences by introducing a new method for large alphabets. We experimentally show that the newly introduced technique is superior to the basic scheme by providing experiments on DNA sequences.

cs.DS↗

A memory versus compression ratio trade-off in PPM via compressed context modeling

Since its introduction prediction by partial matching (PPM) has always been a de facto gold standard in lossless text compression, where many variants improving the compression ratio and speed have been proposed. However, reducing the high space requirement of PPM schemes did not gain that much attention. This study focuses on reducing the memory consumption of PPM via the recently proposed compressed context modeling that uses the compressed representations of contexts in the statistical model. Differently from the classical context definition as the string of the preceding characters at a particular position, CCM considers context as the amount of preceding information that is actually the bit stream composed by compressing the previous symbols. We observe that by using the CCM, the data structures, particularly the context trees, can be implemented in smaller space, and present a trade-off between the compression ratio and the space requirement. The experiments conducted showed that this trade-off is especially beneficial in low orders with approximately 20 - 25 percent gain in memory by a sacrifice of up to nearly 7 percent loss in compression ratio.

cs.DS↗