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Run-length encoding

About: Run-length encoding is a research topic. Over the lifetime, 504 publications have been published within this topic receiving 4441 citations. The topic is also known as: RLE.


Papers
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Proceedings ArticleDOI
03 Mar 1995
TL;DR: Both theoretical and simulation results show that by pre-processing the image, the number of nonzero pixels can be significantly reduced and a more efficient block code is realized.
Abstract: A new block coding technique for the compression of bilevel text images is presented. The technique usesa combination of pre-processing the image to extract the edge information and block coding to compress thedata. The key feature of the technique is simplicity in implementation. The pre-processing consists of an imagedifferencing operation to decorrelate the strings of black pixels. The decorrelation is followed by the losslesscoding of each block of the image. The performance of the new image differencing (ID) method is examinedbased on both theoretical and experimental code length data. Both theoretical and simulation results show thatby pre-processing the image, the number of nonzero pixels can be significantly reduced and a more efficient blockcode is realized.Keywords: block coding, lossless compression, image differencing, run length encoding, fax coding, bilevelimage coding. 1 INTRODUCTION Zeng and Ahmed' and Kavalerchik2 have shown that sparse binary patterns (bilevel images) can be efficientlycoded using a technique known as block coding. Block coding takes advantage of the small number of blackpixels relative to the number of white pixels by breaking the image into blocks and only coding the blocks thatcontain black pixels. Zeng and Ahmed's (ZA) block coding scheme assumes random binary patterns and achievescompression if the density of black pixels is less than 19%. Most text documents are not well modeled as randombinary patterns. In fact, text documents typically contain strings of both black and white pixels in each row ofthe image. Therefore, if these strings are incorporated into the model, an alternative compression scheme maybe used. A two-step method is presented here and will be referred to as the image differencing (ID) block codingmethod. The ID method uses a differencing operation to decorrelate the pixels in a text image and produce datathat is well approximated to a random binary image. After the decorrelation step, a modification of the ZA block

2 citations

Proceedings ArticleDOI
01 Oct 2017
TL;DR: The aim of this work is to offer a detail analysis of lossless compression methods and finding the one which is best suited for compression of multimedia data in cognitive radio environment.
Abstract: This paper considers implementation of audio compression using the lossless compression techniques like dynamic Huffman coding and Run Length Encoding (RLE). Audio file is firstly preprocessed to find sampling frequency and the encoded data bits in sample audio file. After that dynamic Huffman and RLE is applied. The design of dynamic Huffman coding technique involves evaluation of the probabilities of occurrence “on the fly”, as the ensemble is being transmitted and RLE is based on finding the runs of the data i.e. repeating strings and replacing it by single data element and its count. These techniques work with a common goal to obtain the utmost possible compression ratio and less Time Elapsed to compress. The competence of the proposed methods is verified by applying these techniques to variety of audio data. Stimulus behind this work is to offer a detail analysis of lossless compression methods and finding the one which is best suited for compression of multimedia data in cognitive radio environment.

2 citations

Patent
31 Jul 1998
TL;DR: In this article, the problem of prescribed replacement even in the compressed pixel data of decoding images and to exclude the preparation of completely new sub images inside a memory by run length encoding the respective lines of images and occupying the same number of data units for the word of the run length decoding word of an image part for replacing an original image part to be replaced.
Abstract: PROBLEM TO BE SOLVED: To perform prescribed replacement even in the compressed pixel data of decoding images and to exclude the preparation of completely new sub images inside a memory by run length encoding the respective lines of images and occupying the same number of data units for the word of the run length encoding word of an image part for replacing an original image part to be replaced. SOLUTION: It is defined that a memory address for starting a replaceable area is known for the respective lines of a sub image unit. A processing program is provided with a function for retrieving a replaceable part. A reference number 40 is a first replaceable area, a numeral displayed in the figure corresponds to the 16-ary number of the run length encoding of a display character 0 and the reference number 41 indicates a second replaceable area. The areas 40 and 41 are replaced by a character for indicating the display character 2 for instance indicated at the lower part of the figure.

2 citations

Journal Article
TL;DR: A new text Steganography method is proposed that based on a parser and the ASCII of non-printed characters to hide the secret information in the English cover text after coding the secret message and compression it using modified Run Length Encoding method (RLE).
Abstract: Data hiding (Steganography) is a method used for data security purpose and to protect the data during its transmission. Steganography is used to hide the communication between two parties by embedding a secret message inside another cover (audio, text, image or video). In this paper a new text Steganography method is proposed that based on a parser and the ASCII of non-printed characters to hide the secret information in the English cover text after coding the secret message and compression it using modified Run Length Encoding method (RLE). The proposed method achieved a high capacity ratio for Steganography (five times more than the cover text length) when compared with other methods, and provides a 1.0 transparency by depending on some of the similarity measures of Steganography.

2 citations

Journal ArticleDOI
TL;DR: The proposed system introduces a lossless image compression technique based on Run Length Encoding (RLE) that encodes the original magnetic resonance imaging (MRI) image into actual values and their numbers of occurrence that is applied to values array only.
Abstract: Medical image compression is considered one of the most important research fields nowadays in biomedical applications. The majority of medical images must be compressed without loss because each pixel information is of great value. With the widespread use of applications concerning medical imaging in the health-care context and the increased significance in telemedicine technologies, it has become crucial to minimize both the storage and bandwidth requirements needed for archiving and transmission of medical imaging data, rather by employing means of lossless image compression algorithms. Furthermore, providing high resolution and image quality preservation of the processed image data has become of great benefit. The proposed system introduces a lossless image compression technique based on Run Length Encoding (RLE) that encodes the original magnetic resonance imaging (MRI) image into actual values and their numbers of occurrence. The actual image data values are separated from their runs and they are stored in a vector array. Lempel–Ziv–Welch (LZW) is used to provide further compression that is applied to values array only. Finally the Variable Length Coding (VLC) will be applied to code the values and runs arrays for the precise amount of bits adaptively into a binary file. These bit streams are reconstructed using inverse LZW of the values array and inverse RLE to reconstruct the input image. The obtained compression gain is enhanced by 25% after applying LZW to the values array.

2 citations

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Performance
Metrics
No. of papers in the topic in previous years
YearPapers
202123
202020
201920
201828
201727
201624