Fast decoding algorithms for variable-lengths codes

Walder, Jiri; Krátký, Michal; Baca, Radim; Platoš, Jan; Snåšel, Václav

doi:10.1016/j.ins.2011.06.019

Cited by 21 publications

(12 citation statements)

References 18 publications

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“…Therefore, coding systems should be devised while keeping in mind the device in which they will be executed [29], [30]. There are many works concerned with the computational complexity of techniques and algorithms [31], [32] deployed to code 3D images [11], [14], [19], [20], [22], [33]- [36].…”

mentioning

confidence: 99%

Lossy-to-lossless 3D image coding through prior coefficient lookup tables

Aulí-Llinís

Marcellin

Serra-Sagristí

et al. 2013

Information Sciences

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Abstract-This paper describes a low-complexity, highefficiency, lossy-to-lossless 3D image coding system. The proposed system is based on a novel probability model for the symbols that are emitted by bitplane coding engines. This probability model uses partially reconstructed coefficients from previous components together with a mathematical framework that captures the statistical behavior of the image. An important aspect of this mathematical framework is its generality, which makes the proposed scheme suitable for different types of 3D images. The main advantages of the proposed scheme are competitive coding performance, low computational load, very low memory requirements, straightforward implementation, and simple adaptation to most sensors.

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mentioning

confidence: 99%

Lossy-to-lossless 3D image coding through prior coefficient lookup tables

Aulí-Llinís

Marcellin

Serra-Sagristí

et al. 2013

Information Sciences

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“…A prefix code is a variable-size code suitable for coding a set of integers whose size is unknown beforehand. Many researchers such as (Walder, Krátký et al 2012) and (Bača, Walder et al 2010) have shown that prefix encoding approaches give highest compression ratio in comparison to other encoding schemes. In this paper several prefix coding approaches are used for first time to compress XML nodes labels, where each component of a label path is encoded separately and then concatenated (the separators are omitted).…”

Section: Prefix Encoding Methodsmentioning

confidence: 99%

“…Elias-Fibonacci code introduced by (Walder, Krátký et al 2012) as a combination of Elias-Delta code and Fibonacci of order 2 code and it is defined as follow:…”

Section: Elias-fibonacci Of Ordermentioning

confidence: 99%

XML Labels Compression using Prefix-encodings

Zadjali¹,

North

2016

Proceedings of the 12th International Conference on Web Information Systems and Technologies

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Abstract:XML is the de-facto standard for data representation and communication over the web, and so there is a lot of interest in querying XML data and most approaches require the data to be labelled to indicate structural relationships between elements. This is simple when the data does not change but complex when it does. In the day-to-day management of XML databases over the web, it is usual that more information is inserted over time than deleted. Frequent insertions can lead to large labels which have a detrimental impact on query performance and can cause overflow problems. Many researchers have shown that prefix encoding usually gives the highest compression ratio in comparison to other encoding schemes. Nonetheless, none of the existing prefix encoding methods has been applied to XML labels. This research investigates compressing XML labels via different prefix-encoding methods in order to reduce the occurrence of any overflow problems and improve query performance. The paper also presents a comparison between the performances of several prefix-encodings in terms of encoding/decoding time and compressed code size.

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“…Baer [12] provided new lower and upper bounds for the compression rate of binary prefix codes optimized over memoryless sources. Mohajer et al [13] studied the redundancy of Huffman codes whereas Walder et al [14] provided algorithms for fast decoding of variable length codes.…”

Section: Introductionmentioning

confidence: 99%

“…We also use relation (14) to find probability distribution ( ) 1 2 , , , n p p p  for Tsallis entropy which is close to uniform distribution. …”

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confidence: 99%

An Algorithm to Generate Probabilities with Specified Entropy

Parkash¹,

Kakkar²

2015

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The present communication offers a method to determine an unknown discrete probability distribution with specified Tsallis entropy close to uniform distribution. The relative error of the distribution obtained has been compared with the distribution obtained with the help of mathematica software. The applications of the proposed algorithm with respect to Tsallis source coding, Huffman coding and cross entropy optimization principles have been provided.

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Fast decoding algorithms for variable-lengths codes

Cited by 21 publications

References 18 publications

Lossy-to-lossless 3D image coding through prior coefficient lookup tables

Lossy-to-lossless 3D image coding through prior coefficient lookup tables

XML Labels Compression using Prefix-encodings

An Algorithm to Generate Probabilities with Specified Entropy

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