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.

A Context-Based Video Compression: A Quantum-Inspired Vector Quantization Approach.

Abstract: This paper proposes a modified video compression model that optimizes vector quantization codebook by using the adapted Quantum Genetic Algorithm (QGA) that uses the quantum features, superposition, and entanglement to build optimal codebook for vector quantization. A context-based initial codebook is created by using a background subtraction algorithm; then, the QGA is adapted to get the optimal codebook. This optimal feature vector is then utilized as an activation function inside the neural network’s hidden layer to remove redundancy. Furthermore, approximation wavelet coefficients were lossless compressed with Differential Pulse Code Modulation (DPCM); whereas details coefficients are lossy compressed using Learning Vector Quantization (LVQ) neural networks. Finally, Run Length Encoding is engaged to encode the quantized coefficients to achieve a high compression ratio. As individuals in the QGA are actually the superposition of multiple individuals, it is less likely that good individuals will be lost. Experiments have proven the system’s ability to achieve a higher compression ratio with acceptable efficiency measured by PSNR.

Performance Analysis of Data Compression Using Lossless Run Length Encoding

Abstract: In a recent era of modern technology, there are many problems for storage, retrieval and transmission of data. Data compression is necessary due to rapid growth of digital media and the subsequent need for reduce storage size and transmit the data in an effective and efficient manner over the networks. It reduces the transmission traffic on internet also. Data compression try to reduce the number of bits required to store digitally. The various data and image compression algorithms are widely use to reduce the original data bits into lesser number of bits. Lossless data and image compression is a special class of data compression. This algorithm involves in reducing numbers of bits by identifying and eliminating statistical data redundancy in input data. It is very simple and effective method. It provides good lossless compression of input data. This is useful on data that contains many consecutive runs of the same values. This paper presents the implementation of Run Length Encoding for data compression. Article History Received: 17 July 2017 Accepted:09 August 2017

Drawing server, center server, encoding apparatus, control method, encoding method, program, and recording medium

Abstract: PROBLEM TO BE SOLVED: To perform an efficient memory check utilizing encoding processing.SOLUTION: Data to which parity information is added regarding a memory to be checked are written in the memory, the data are then read from the memory, run length encoding processing is performed thereon and encoded data are generated. An encoding apparatus refers to a bit stream regarding the written data and, when generating the encoded data, compares the bit stream with the added parity information to detect a bit inversion error.

Quantized Run Length Encoding QRLE -New Compression Method-: Application to ECG Transmission via IEEE802.11b WLAN Channel

Abstract: A new method of signal compression, called Quantized Run Length Encoding QRLE, based upon the ‘classical’ run length method combined to discrete wavelet transform thresholding planned for a transmission via the WiFi IEEE 802.11b WLAN channel, simulated by Simulink/Matlab Integrated Development Environment IDE, is presented in this work. The key idea of our new method consists of quantifying each pair of zero followed by its corresponding run number, issued from the RLE, by one value consisting on the run number value plus an offset of predetermined integer value. In this work, the suitable offset was adjusted to 1024(≡210). This leads to quantizing the ‘new’ run number on 11 bits while the non null ECG thresholded ECG signal samples are quantized on 10 bits. The trivial advantage of this method is suppression, and consequently gaining, of all zeros. We have applied the proposed algorithm to extracted ECG signals, from the universal MIT-BIH arrhythmia data base. To evaluate the performance of the new QRLE compression method, we have simulated transmitting the compressed ECG signal via the Simulink/Maltab IEEE 802.11b WLAN noisy channel model. Besides the perceptible visual inspection, three criteria are used, in our evaluation phase, that are: compression ratio –CR-, the normalized root mean squared error (NRMSE), and the difference between the original and reconstructed versions of the ECG signal. The obtained results are around 10:1 in terms of CR, 0.07 in terms of NRMSE and a difference of about (10−4) ± 0.02. Moreover, a comparative study with respect to selected ECG compression algorithms show the higher performance of the developed new technique called ‘Quantized run length encoding QRLE’.