234 research outputs found
Prefix Codes for Power Laws with Countable Support
In prefix coding over an infinite alphabet, methods that consider specific
distributions generally consider those that decline more quickly than a power
law (e.g., Golomb coding). Particular power-law distributions, however, model
many random variables encountered in practice. For such random variables,
compression performance is judged via estimates of expected bits per input
symbol. This correspondence introduces a family of prefix codes with an eye
towards near-optimal coding of known distributions. Compression performance is
precisely estimated for well-known probability distributions using these codes
and using previously known prefix codes. One application of these near-optimal
codes is an improved representation of rational numbers.Comment: 5 pages, 2 tables, submitted to Transactions on Information Theor
Efficient Fully-Compressed Sequence Representations
We present a data structure that stores a sequence over alphabet
in n\Ho(s) + o(n)(\Ho(s){+}1) bits, where \Ho(s) is the
zero-order entropy of . This structure supports the queries \access, \rank\
and \select, which are fundamental building blocks for many other compressed
data structures, in worst-case time \Oh{\lg\lg\sigma} and average time
\Oh{\lg \Ho(s)}. The worst-case complexity matches the best previous results,
yet these had been achieved with data structures using n\Ho(s)+o(n\lg\sigma)
bits. On highly compressible sequences the bits of the
redundancy may be significant compared to the the n\Ho(s) bits that encode
the data. Our representation, instead, compresses the redundancy as well.
Moreover, our average-case complexity is unprecedented. Our technique is based
on partitioning the alphabet into characters of similar frequency. The
subsequence corresponding to each group can then be encoded using fast
uncompressed representations without harming the overall compression ratios,
even in the redundancy. The result also improves upon the best current
compressed representations of several other data structures. For example, we
achieve compressed redundancy, retaining the best time complexities, for
the smallest existing full-text self-indexes; compressed permutations
with times for and \pii() improved to loglogarithmic; and
the first compressed representation of dynamic collections of disjoint
sets. We also point out various applications to inverted indexes, suffix
arrays, binary relations, and data compressors. ..
Huffman source coding
Abstract. In this work, A Huffman source coding system is studied and implemented. The work will go through the basics of the source coding theorem, standard Huffman code is introduced, its weaknesses in a practical system are presented, and finally, methods and algorithms are introduced to overcome these weaknesses. In Particular, the preset dictionaries and Vitter algorithm are introduced. Then, the implementation is presented and the performance is studied by compressing text files.Huffman lÀhteenkoodaus. TiivistelmÀ. TÀssÀ työssÀ tutkitaan ja toteutetaan Huffman lÀhteenkoodaus jÀrjestelmÀ. TyössÀ kÀydÀÀn lÀpi lÀhteenkoodauksen teoriaa, standardi Huffman koodaus, sen heikkoudet kÀytÀnnön jÀrjestelmÀssÀ, ja lopuksi keinoja nÀiden heikkouksien yli pÀÀsemiseksi. Erityisesti huomioidaan etukÀteen lasketut lÀhdekoodit ja dynaaminen Vitter algoritmi. Lopuksi työ toteutetaan ohjelmistona ja eri koodaustapoja verrataan keskenÀÀn kompressoimalla tekstitiedostoja
Arithmetic coding revisited
Over the last decade, arithmetic coding has emerged as an important compression tool. It is now the method of choice for adaptive coding on multisymbol alphabets because of its speed,
low storage requirements, and effectiveness of compression. This article describes a new implementation of arithmetic coding that incorporates several improvements over a widely used earlier version by Witten, Neal, and Cleary, which has become a de facto standard. These improvements include fewer multiplicative operations, greatly extended range of alphabet sizes and symbol probabilities, and the use of low-precision arithmetic, permitting implementation by fast shift/add operations. We also describe a modular structure that separates the coding, modeling, and probability estimation components of a compression system. To motivate the improved coder, we consider the needs of a word-based text compression program. We report a range of experimental results using this and other models. Complete source code is available
Gbit/second lossless data compression hardware
This thesis investigates how to improve the performance of lossless data compression hardware
as a tool to reduce the cost per bit stored in a computer system or transmitted over a
communication network.
Lossless data compression allows the exact reconstruction of the original data after
decompression. Its deployment in some high-bandwidth applications has been hampered due to
performance limitations in the compressing hardware that needs to match the performance of the
original system to avoid becoming a bottleneck. Advancing the area of lossless data compression
hardware, hence, offers a valid motivation with the potential of doubling the performance of the
system that incorporates it with minimum investment.
This work starts by presenting an analysis of current compression methods with the objective of
identifying the factors that limit performance and also the factors that increase it. [Continues.
A high-speed distortionless predictive image-compression scheme
A high-speed distortionless predictive image-compression scheme that is based on differential pulse code modulation output modeling combined with efficient source-code design is introduced. Experimental results show that this scheme achieves compression that is very close to the difference entropy of the source
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