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

Context-based adaptive variable length coding (CAVLC) is a new and efficient entropy coding tool for the H.264/AVC. Although the CAVLC provides the excellent compression ratio, the computational complexity of the CAVLC decoder is higher than the traditional variable length decoder. This report proposes a low-power and low-cost architecture of the CAVLC decoder for the H.264/AVC baseline profile. The research derives the optimum power model for the variable length LUT of the CAVLC decoder, and then we merge the codeword length to reduce the hardware cost among the different LUT. Moreover, the design is based on the 0.18-μm TSMC CMOS technology. The experimental results show that the proposed decoder operates at the 125 MHz clock frequency with the hardware cost of 4412 gates. Furthermore, the proposed design can reduce the power consumption about 44% to 48% more than the previous low-power CAVLD schemes did.

The exponential growth in remote sensing, coupled with advancements in integrated circuits (IC) design and fabrication technology for communication, has prompted the progress of Wireless Sensor Networks (WSN). WSN comprises of sensor nodes and hubs fit for detecting, processing, and communicating remotely. Sensor nodes have limited resources such as memory, energy and computation capabilities restricting their ability to process large volume of data that is generated. Compressing the data before transmission will help alleviate the problem. Many data compression methods have been proposed but mainly for image processing and a vast majority of them are not pertinent on sensor nodes because of memory impediment, energy utilization and handling speed. To overcome this issue, authors in this research have chosen Run Length Encoding (RLE) and Adaptive Huffman Encoding (AHE) data compression techniques as they can be executed on sensor nodes. Both RLE and AHE are capable of balancing compression ratio and energy utilization. In this paper, a hybrid method comprising RLE and AHE, named as H-RLEAHE, is proposed and further investigated for sensor nodes. In order to verify the efficacy of the data compression algorithms, simulations were run, and the results compared with the compression techniques employing RLE, AHE, H-RLEAHE, and without the use of any compression approach for five distinct scenarios. The results demonstrate the RLE’s efficiency, as it surpasses alternative data compression methods in terms of energy efficiency, network speed, packet delivery rate, and residual energy throughout all iterations.

Data Compression can save some storage space and accelerate data transfer. Among many compression algorithm, Run Length Encoding (RLE) is a simple and fast algorithm. RLE can be used to compress many types of data. However, RLE is not very effective for image lossless compression because there are many little differences between neighboring pixels. This research proposes a new lossless compression algorithm called YRL that improve RLE using the idea of Relative Encoding. YRL can treat the value of neighboring pixels as the same value by saving those little differences / relative value separately. The test done by using various standard image test shows that YRL have an average compression ratio of 75.805% for 24-bit bitmap and 82.237% for 8-bit bitmap while RLE have an average compression ratio of 100.847% for 24-bit bitmap and 97.713% for 8-bit bitmap.

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.

BackgroundAdvanced sequencing machines dramatically speed up the generation of genomic data, which makes the demand of efficient compression of sequencing data extremely urgent and significant. As the most difficult part of the standard sequencing data format FASTQ, compression of the quality score has become a conundrum in the development of FASTQ compression. Existing lossless compressors of quality scores mainly utilize specific patterns generated by specific sequencer and complex context modeling techniques to solve the problem of low compression ratio. However, the main drawbacks of these compressors are the problem of weak robustness which means unstable or even unavailable results of sequencing files and the problem of slow compression speed. Meanwhile, some compressors attempt to construct a fine-grained index structure to solve the problem of slow random access decompression speed. However, they solve the problem at the sacrifice of compression speed and at the expense of large index files, which makes them inefficient and impractical. Therefore, an efficient lossless compressor of quality scores with strong robustness, high compression ratio, fast compression and random access decompression speed is urgently needed and of great significance.ResultsIn this paper, based on the idea of maximizing the use of hardware resources, LCQS, a lossless compression tool specialized for quality scores, was proposed. It consists of four sequential processing steps: partitioning, indexing, packing and parallelizing. Experimental results reveal that LCQS outperforms all the other state-of-the-art compressors on all criteria except for the compression speed on the dataset SRR1284073. Furthermore, LCQS presents strong robustness on all the test datasets, with its acceleration ratios of compression speed increasing by up to 29.1x, its file size reducing by up to 28.78%, and its random access decompression speed increasing by up to 2.1x. Additionally, LCQS also exhibits strong scalability. That is, the compression speed increases almost linearly as the size of input dataset increases.ConclusionThe ability to handle all different kinds of quality scores and superiority in compression ratio and compression speed make LCQS a high-efficient and advanced lossless quality score compressor, along with its strength of fast random access decompression. Our tool LCQS can be downloaded from https://github.com/SCUT-CCNL/LCQSand freely available for non-commercial usage.

Data compression is needed so that the need for storage media and data transfer time becomes more efficient. This study compressed image data using the Run-Length Encoding (RLE) method. The test data is the original image (gray scale) and the image results of improving image quality (image enhancement) using contrast modification. Modification of contrast using contrast stretching methods. Through experiments wanting to know the extent to which the RLE method works less effectively for images with complex color intensity. The image of contrast modification results has a more complex color intensity or more varied pixel value. Obtained the number of pairs (p, q) RLE in the image of contrast modification results is less than the original image, with the pair ratio (p, q) RLE ranges from 0.64% to 1.59%. Although this image has a more varied pixel value than its original image, it can produce a compression ratio of the number of pairs (p, q) RLE.

Image compression plays a crucial role in optimising storage and transmission efficiency. This paper evaluates the performance of Run-Length Encoding (RLE), Huffman Coding, and Lempel-Ziv-Welch (LZW) algorithms for compressing grayscale PNG and JPG images. The study analyses their effectiveness using compression ratio, bits per pixel, and compression time as key performance metrics. Results indicate that LZW achieved the highest compression ratio, ranging from 1.0113 to 2.4020, making it the most efficient for file size reduction. RLE performed moderately, with compression ratios between 0.5456 and 2.3895, while Huffman Coding exhibited the lowest ratios, ranging from 0.2646 to 1.0680. In terms of bits per pixel, LZW recorded the lowest values, highlighting its ability to reduce data while preserving image quality. Compression time analysis revealed that RLE was the fastest, with processing times between 0.0019 and 0.0468 seconds, making it suitable for real-time applications. LZW and Huffman Coding demonstrated a trade-off between compression efficiency and speed. These findings establish LZW as the most effective algorithm for high compression with minimal quality loss, while RLE remains the best option for speed-critical applications.

ObjectiveData compression is a useful process in tele-monitoring applications, in which lesser number of bits are needed to represent the same data. In this work, a run-time lossless compression of single-channel Electrocardiogram (ECG) and Photoplethysmogram (PPG) signals is proposed, maintaining all dominant features. MethodsThe single-channel data are first quantized using optimal quantization level, so that fewer number of bits are needed to represent it, maintaining low quantization error. Then, second order delta encoding and run-length encoding (RLE) based data compression are proposed in this work. A new approach of using ‘buffer array’ along with RLE is also introduced, so that minimum bits are needed to store. ResultsThis algorithm was tested on various single-lead ECG and PPG signals available in Physionet. An average compression ratio (CR) was achieved of 6.52, 3.82, and 2.49 for 547 PTBDB ECG records, 48 MITDB ECG records, and 53 MIMIC-II PPG records, respectively. This algorithm was also performed on single-channel ECG, collected from 10 healthy volunteers using AD8232 ECG module, with 125 Hz sampling frequency and 10-bit data resolution, which resulted in average CR of 2.34. ConclusionThis algorithm was also performed on a smartphone device that provided user-friendly operation. The low computational complications and standalone operation of data collection, compression, and transmission encouraged its implementation for run-time operation. SignificanceA comparative study of the proposed work with previously published works proved this fact that this algorithm provided better performance in the area of run-time patient health monitoring applications.

Compression purpose to reduce the redundancy data as small as possible and speed up the data transmission process. To solve the size problem in saving data and transmission process, we use Run Length Encoding and Fibonacci Code algorithm to do compression process. Run Length Encoding and Fibonacci Code algorithm is a type of lossless data compression used in this research, which performance will be measured by comparison parameters of the Compression Ratio (CR), Redundancy (RD), Space Saving (SS) and Compression Time. The compression process is only done on image files with Bitmap format (*.bmp) and encode using Run Length Encoding or Fibonacci Code, then perform the compression process. The final result of the compression is file with extension *.rle or *.fib which contains compressed information that can be decompressed back. The output of the decompression result is an original image file that is stored with *.bmp extension. Fibonacci algorithm will give a better compressed size on image color, while in a grayscale image Run Length Encoding will give a better compressed size. Based on the results of research at two different types of images, each algorithm has its own advantages. Fibonacci Code algorithm is better for color image compression while Run-Length algorithm Encoding is better for grayscale image compression.

Text data compression is done to make the file size smaller. Algorithm is a sequence of steps that aims to solve a problem. In this text data compress research using the Run Length Encoding (RLE) data compress method, it can be used to trim data to minimize the use of storage space so that it can be utilized better. As the function of the data compress itself to trim the file, its use is very beneficial for future technology. The programming applied is a python application, applying the concept of structured programming. Structured programming is a programming concept or paradigm that solves problems structurally, without looking at objects or divisions but must be structured, The result of this research is that the phyton application is able to trim text data so as to minimize the use of storage space so that it can be utilized better.

A prototype chip, called RD53A, has been designed by the RD53 collaboration to face the very high hit and trigger rate requirements (up to 3 GHz/cm2 and 1 MHz, respectively) of the High Luminosity LHC experiment upgrades. In this paper, an improved algorithm for data compression, capable of sustaining the very high data volume and proposed to be implemented in the periphery of the chip, is presented: it exploits Run Length Encoding (RLE) and Variable Length Coding (VLC) to compact chip pixel hit patterns. The compression and decompression algorithms are implemented with MATLAB, and the performance is calculated taking into account the RD53A data readout implementation and its chip simulation and verification framework (called VEPIX53). In all considered cases, the results show that the RLE and VLC combination achieves a data compression ratio between 1.57 and 1.62, resulting in a bitstream size reduction between 36.2% and 38.4% with respect to the rate of the current data transmission format.

A real-time data processing algorithm based on run-length encoding (RLE) for an intelligent racing vehicle is introduced in this paper. In order to improve the achievement of the intelligent racing vehicle's running in the second loop by recoding route information, a new method based on RLE is provided by setting an optimal calculus threshold. Simulated by Matlab/Simulink, the route memorization algorithm shows the obvious advantage in the data compression, which makes the compression ratio up to 22.3. In the meantime, the calculus threshold can be constructed in a certain range, which qualifies the algorithm with a good robustness. Compared with the optimal results achieved by Matlab's genetic algorithm and direct search toolbox, the RLE algorithm can satisfy the requirements very well. When the embedded system has to face the flood for the data increasing geometrically, this high-quality real-time algorithm has been demonstrated with great practical potential.

This paper presents an efficient electrocardiogram (ECG) data compression and transmission algorithm based on discrete wavelet transform and run length encoding. The proposed algorithm provides comparatively high compression ratio and low percent root-mean-square difference values. 48 records of ECG signals are taken from MIT-BIH arrhythmia database for performance evaluation of the proposed algorithm. Each record of ECG signals are of duration one minute and sampled at sampling frequency of 360 Hz over 11-bit resolution. Discrete wavelet transform has been used by means of linear orthogonal transformation of original signal. Using discrete wavelet transform, signal can be analyzed in time and frequency domain both. It also preserves the local features of the signal very well. After thresholding and quantization of wavelet transform coefficients, signals are encoded using run length encoding which improves compression significantly. The proposed algorithm offers average values of compression ratio, percentage root mean square difference, normalized percentage root mean square difference, quality score and signal to noise ratio of 44.0, 0.36, 5.87, 143, 3.53 and 59.52 respectively over 48 records of ECG data.

Improvement of data compression technology for power dispatching based on run length encoding

In this paper, a new approach to remove noise present in ECG signal is proposed. Baseline wander and high frequency noise is eliminated by using computationally efficient linear phase filter ie. interpolated finite impulse response (IFIR) filter. The IFIR filter is designed by using Kaiser window function to achieve high stop band attenuation. As compared to other methods, the technique presented could achieve a reduction in computational complexity by 80.14 percent. Data compression is also performed in this study using wavelet packet decomposition along with Run-length encoding. Run-length encoding is used to improve the compression performance. For evaluation of the performance of IFIR filter, computational cost reduction (CRC) parameter is used, which directly depended on multipliers and adders. Different fidelity factors are considered to evaluate the performance of the proposed data compression method, viz., compression ratio (CR), signal to noise ratio (SNR), retained energy (RE) and percent root mean square difference (PRD), their magnitude being 25.13, 38.93, 99.10 and 1.75, respectively. MIT-BIH arrhythmia database has been utilized to judge the entire set of computations mentioned above noise removal and ECG signal compression. This work also includes beat detection of original and reconstructed signals. Simulated results show that decompressed signal is a replica of the input signal.