Linear-time construction of optimal context trees

Helfgott, Harald Andrés; Cohn, M. J.

doi:10.1109/dcc.1998.672167

Cited by 6 publications

(2 citation statements)

References 2 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…As for encoding complexity, which is of great interest for DSC, DAC has linear encoding complexity, like a classical AC [41]. Turbo codes and the LDPC codes in [21] also have linear encoding complexity, whereas general LDPC codes typically have more than linear, and typically quadratic complexity [42].…”

Section: Discussionmentioning

confidence: 99%

Distributed Arithmetic Coding for the Asymmetric Slepian-Wolf problem

Grangetto,

Magli,

Olmo

2007

Preprint

View full text Add to dashboard Cite

Distributed source coding schemes are typically based on the use of channels codes as source codes. In this paper we propose a new paradigm, named "distributed arithmetic coding", which extends arithmetic codes to the distributed case employing sequential decoding aided by the side information. In particular, we introduce a distributed binary arithmetic coder for the Slepian-Wolf coding problem, along with a joint decoder. The proposed scheme can be applied to two sources in both the asymmetric mode, wherein one source acts as side information, and the symmetric mode, wherein both sources are coded with ambiguity, at any combination of achievable rates. Distributed arithmetic coding provides several advantages over existing Slepian-Wolf coders, especially good performance at small block lengths, and the ability to incorporate arbitrary source models in the encoding process, e.g., context-based statistical models, in much the same way as a classical arithmetic coder. We have compared the performance of distributed arithmetic coding with turbo codes and low-density parity-check codes, and found that the proposed approach is very competitive.

show abstract

Section: Discussionmentioning

confidence: 99%

Distributed Arithmetic Coding for the Asymmetric Slepian-Wolf problem

Grangetto,

Magli,

Olmo

2007

Preprint

View full text Add to dashboard Cite

show abstract

“…where K is the cardinality of the tree T. We will denote the set of all terminal nodes as ) (T S . Let us denote as n i (s), s∈S(T), the count of the symbol i, encoded by the statistical model, pointed by the terminal node s. By the cost of a terminal node s here we understand the following expression [7], [13]:…”

Section: Construction Of Optimal Context Treementioning

confidence: 99%

Lossless Compression of Map Contours by Context Tree Modeling of Chain Codes

2005

View full text Add to dashboard Cite

We consider lossless compression of digital contours in map images. The problem is attacked by the use of context-based statistical modeling and entropy coding of chain codes. We propose to generate an optimal context tree by first constructing a complete tree up to a predefined depth, and then create the optimal tree by pruning out nodes that do not provide improvement in compression. Experiments show that the proposed method gives lower bit rates than the existing methods for the set of test images.

show abstract

Context tree compression of multi-component map images

Kopylov

Fränti

Proceedings DCC 2002. Data Compression Conference

View full text Add to dashboard Cite

Linear-time construction of optimal context trees

Cited by 6 publications

References 2 publications

Distributed Arithmetic Coding for the Asymmetric Slepian-Wolf problem

Distributed Arithmetic Coding for the Asymmetric Slepian-Wolf problem

Lossless Compression of Map Contours by Context Tree Modeling of Chain Codes

Context tree compression of multi-component map images

Contact Info

Product

Resources

About