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.

Image Compression Using Run Length Encoding (RLE)

Abstract: The goal of image compression is to remove the redundancies by minimizing the number of bits required to represent an image. It is used for reducing the redundancy that is nothing but avoiding the duplicate data. It also reduces the storage memory to load an image. Image Compression algorithm can be Lossy or Lossless. In this paper, DCT and DWT based image compression algorithms have been implemented using MATLAB platform. Then, the improvement of image compression through Run Length Encoding (RLE) has been achieved. The three images namely Baboon, Lena and Pepper have been taken as test images for implementing the techniques. Various image objective metrics namely compression ratio, PSNR and MSE have been calculated. It has been observed from the results that RLE based image compression achieves higher compression ratio as compared with DCT and DWT based image compression algorithms.

An Enhanced Run Length Encoding using an Elegant Pairing Function for Medical Image Compression

Abstract: Medical image compression is in high demand in the medical field, since it reduces the required time to transmit an image in a bandwidth or store an image for medical purposes. Modern medical image diagnostics involves a large set of medical images that are often based on MRI, XRays or CT. However, the introduction of new technologies and the fast development of existing ones has resulted in a high demand for bandwidth. In this article, the proposed image compression method for medical images it uses the discrete wavelet transform as a compression tool, which provides a better compression ratio without losing valuable information and through the implementation of the proposed enhanced run length encoder, by the use of the elegant pairing function, we can achieve higher compression ratios and high visual quality. Despite this, the fidelity is subject to the capabilities and limitations of the Human Visual System.

A Design for An Efficient Hybrid Compression System for EEG Data

Abstract: The Electroencephalography (EEG) signals that indicate the electrical activity of the brain are acquired with a high sampling rate. Consequently, the size of the recorded EEG data is large. For storing and transmitting these data, large space and bandwidth are demanded. Therefore, preprocessing and compressing EEG data are important for efficient data transmission and storage. The purpose of this approach is to design an efficient EEG data compression system in terms of time and space complexities. The proposed system consists of three main units: preprocessing unit, compression unit, and reconstruction unit. The core of the compression process occurs in the compression unit. Different combinations of hybrid lossy/lossless compression techniques were tried in the compression process. In this study, both the Discrete Cosine Transform and the Discrete Wavelet Transform techniques were experimented for the lossy compression algorithm. The Arithmetic Coding and the Run Length Encoding were experimented then for the lossless compression algorithm. The final results showed that combining both the Discrete Cosine Transform and the Run Length Encoding yields the most optimal system complexity and compression ratio. This approach achieved up to CR = 94% at RMSE = 0.188.

Method based on quarter-tree decomposition of image compression

Abstract: Nowadays,with the rapid development of the information society,image has become one of the important carriers which can transmits information.Because of the massive content of information of unprocessed image,it is extremely important to try our best to research and develop the technology for image coding.This main topic is to explore for a large area of continuous shadow of the same color or image compression.We use quarter-tree decomposition of image compression methods to image compression because of the deficiencies of the RLE algorithm.Experiment indicates that the improved technique which uses quarter-tree decomposition of image compression methods can greatly improve compression efficiency.The technique presents a new approach for the technique of static image compression.

Method of compression of SOC test data based on alternative run-length encoding

Abstract: Aiming at the reduction of SOC test time and test data volume,this paper introduces quantum evolutionary algorithm to complete establishing the module and designing the algorithm for hierarchical SOC under the condition of the power constraint and get the corresponding test set.In succession,the test set of multiple cores is integrated through share broadcast technology,and then the method of alternative run-length encoding is combined to compress the test set.This method takes into account both the"0"and"1"run length,so it can greatly reduce the number of the shorter run-length.The experimental results for SOC benchmark show that:Compared with other algorithm,quantum algorithm can efficiently meet the demand of test power while it acquires shorter test time.Compared with other compression encoding method,the method of this paper acquires a more effective compression.