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.

Method and data structure for compressing file-reference information

Abstract: Compression of local data uses various methods to encode location data (e.g. line numbers and, optionally, column numbers) representing references to a source code information symbol in a source code file. Run length encoding and other run encoding methods are used. A preferred run encoding method encodes a run-encoded bitmap comprising a set of runs, in which each run has a first run field that provides a starting line for the run, and a second run field that encodes as a bitmap the remaining lines in the run in which the reference appears. A run length field may be used to indicate the length of the second run field. Another run field may be used to encode the length of the first run field, to permit further compression.

A lossless compression algorithm for vibration data of space systems

Abstract: Space systems demand some of the most precise and accurate technology. The body of space systems are subjected to many vibrations and shocks. To make sure their stable operation, these vibrations are monitored continuously at a higher sampling rate which results in large amount of data. The data is transmitted over a wireless network to the ground station. Therefore data compression becomes unavoidable, not only for efficient utilization of transmission bandwidth but also for reduced storage requirements. Even the smallest vibration is critical in space applications. So lossless compression techniques are preferred. In this work, a lossless compression technique is proposed where the input data is split into two sets which helps in attaining data redundancy and then the algorithm is applied. Modified Move to front(MTF) coding is done on this data, where positional values of a dictionary are transmitted instead of the sample value. Run length encoding(RLE) is done on the MTF coded data. Next all the successively repeating number patterns in the data set is recognized and RLE coded. The compression ratio obtained for data without and with noise are 4.15 and 2.75 respectively.

The Relationship between Euclid’s Algorithms and Run-Length Encoding

Abstract: The sequence of plotter moves generated by Bresenham’s algorithm for a single straight line can be expressed recursively as a sequence of repeated patterns A similar pattern can be seen in the flow of Euclid’s algorithm if it is applied to find the highest common factor of the two integer parameters defining the gradient and end point of the given line The analysis suggests a simple extension of Bresenham’s algorithm which provides an automatic shortcut (Computer Journal 25, 114)

Image Compression using Run Length Encoding and its Optimisation

Abstract: Images are among the most common and popular representations of data. Digital images are used for professional and personal use ranging from official documents to social media. Thus, any Organization or individual needs to store and share a large number of images. One of the most common issues associated with using images is the potentially large file-size of the image. Advancements in image acquisition technology and an increase in the popularity of digital content means that images now have very high resolutions and high quality, inevitably leading to an increase in size. Image compression has become one of the most important parts of image processing these days due to this. The goal is to achieve the least size possible for an image while not compromising on the quality of the image, that gives us the perfect balance. Therefore, to achieve this perfect balance many compression techniques have been devised and it is not possible to pinpoint the best one because it is really dependent on the type of image to be compressed. So here we are going to elaborate on converting images into binary images and the Run length Encoding (RLE) algorithm used for compressing binary images. Now, RLE is itself a very effective and simple approach for compression of images but, sometimes, the size of an image actually increases after RLE algorithm is applied to the image and this is one of the major drawbacks of RLE. In this research paper we are going to propose an extension or maybe an upgradation to RLE method which will ensure that the size of an image never exceeds beyond its original size, even in the worst possible scenario

Image compression: wavelet-type transform along generalized scan

Abstract: An efficient method for visual data compression is presented, combining generalized Peano Scan, wavelet decomposition and adaptive sub-band coding technique. The Peano Scan which is an application of the Peano curve to the scanning of images, is incorporated with the encoding scheme in order to cluster highly correlated pixels. Using wavelet decomposition, an adaptive sub-band coding technique is developed to encode each sub-band separately with an optimum algorithm. Discrete Cosine Transform (DCT) is applied on the low spatial frequency sub-band, and high spatial frequency sub-bands are encoded using Run Length encoding technique.