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.

The Methods of Coding in Image Compression

Abstract: First,several typical ways of image coding are explained in the paper,including Run Length Encoding,Huffman Coding,LZW and DCT.Then some new generation techniques based on fractal coding,wavelet coding and artificial neural network coding are presented.

The PHY-NGSC-Based ORT Run Length Encoding Scheme for Video Compression

Abstract: This paper proposes a compression algorithm using octonary repetition tree (ORT) based on run length encoding (RLE). Generally, RLE is one type of lossless data compression method which has duplica...

Index Compression for Audio Fingerprinting Systems Based on Compressed Suffix Array

Abstract:  Abstract—As one of most popular technologies, audio fingerprinting has recently attracted much attention in music retrieval systems. In music retrieval methods based on audio fingerprints, a large database is required in order to compare the fingerprints extracted from the query. In other words, the efficient search method has to be developed. In this paper, we propose a method for index compression using a compressed suffix array. Taking advantage of the fact that the repetitive characters occur frequently in higher bits of the sorted audio fingerprint data, the proposed method compresses the index by encoding the 8-bit data sequences by Run Length Encoding. Vertical Code is also used to compress the array, wherein the positions of the sorted data are stored. Four sets of music databases are used in experiments to evaluate the effectiveness of the proposed method. The experimental results show that the proposed method, compared with the conventional method, only needs 30% of the space of an audio fingerprints database for a music database consisting of 8000 songs, and around 80% of the index space for a database of 1000 songs. Moreover, the entire space cost is reduced to around 60%, compared with the method based on the suffix array. using a suffix array (6) has also been proposed. In the method based on the suffix array, the space cost increases in proportion to the growing music database. In this paper, we proposed a method to reduce the space cost by compressing the index of the database. The paper is organized as follows: Section II outlines music retrieval based on audio fingerprints. We review a fast Hamming space search method (7) based on a suffix array in Section III, and propose a space-saving method based on a compressed suffix array in Section IV. We evaluate the proposed method in Section V. Finally, the conclusions and future work are given in Section VI.

Hybrid Compression Techniques for EEG Data Based on Lossy/Lossless Compression Algorithms

Abstract: The recorded Electroencephalography (EEG) data comes with a large size due to the high sampling rate. Therefore, large space and more bandwidth are required for storing and transmitting the EEG data. Thus, preprocessing and compressing the EEG data is a very important part in order to transmit and store it efficiently with less bandwidth and less space. The objective of this paper is to develop an efficient system for EEG data compression. In this system, the recorded EEG data are firstly preprocessed in the preprocessing unit. Standardization and segmentation of EEG data are done in this unit. Then, the resulting EEG data are passed to the compression unite. The compression unit composes of a lossy compression algorithm followed by a lossless compression algorithm. The lossy compression algorithm transforms the randomness EEG data into data with high redundancy. Subsequently, A lossless compression algorithm is added to investigate the high redundancy of the resulting data to get high Compression Ratio (CR) without any additional loss. In this paper, the Discrete Cosine Transform (DCT) and Discrete Wavelet Transform (DWT) are proposed as a lossy compression algorithm. Furthermore, Arithmetic Encoding and Run Length Encoding (RLE) are proposed as a lossless compression algorithm. We calculate the total compression and reconstruction time (T), Root Mean Square Error (RMSE), and CR in order to evaluate the proposed system. Simulation results show that adding RLE after the DCT algorithm gives the best performance in terms of compression ratio and complexity. Using the DCT as a lossy compression algorithm followed by the RLE as a lossless compression algorithm gives CR=90% at RMSE=0.14 and more than 95% of CR at RMSE=0.2.