scholarly journals Lossless Data Compression Based on Adaptive Linear Predictor for Embedded System of Unmanned Vehicles

2017 ◽  
Vol 34 (11) ◽  
pp. 2499-2508
Author(s):  
Fangjie Yu ◽  
Linhua Li ◽  
Yang Zhao ◽  
Mengmeng Wang ◽  
Guilin Liu ◽  
...  
Keyword(s):  
Embedded System ◽  
Data Compression ◽  
Lossless Compression ◽  
Unmanned Vehicles ◽  
Compression Algorithm ◽  
Observation Data ◽  
Mobile Platforms ◽  
Technical Improvement ◽  
Linear Predictor ◽  
Compression Performance

AbstractUnmanned vehicles represent a significant technical improvement for ocean and atmospheric monitoring. With the increasing number of sensors mounted on the unmanned mobile platforms, the data volume and its rapid growth introduce a new challenge relative to the limited transmission bandwidth. Data compression provides an effective approach. However, installing a lossless compression algorithm in an embedded system, which is in fact limited in computing resources, scale, and energy consumption, is a challenging task. To address this issue, a novel self-adaptive lossless compression algorithm (SALCA) that is focused on the dynamic characteristics of multidisciplinary ocean and atmospheric observation data is proposed that is the extended work of two-model transmission theory. The proposed method uses a second-order linear predictor that can be changed as the input data vary and can achieve better lossless compression performance for dynamic ocean data. More than 200 groups of conductivity–temperature–depth (CTD) profile data from underwater gliders are used as the standard input, and the results show that compared to two state-of-the-art compression methods, the proposed compression algorithm performs better in terms of compression ratio and comprehensive power consumption in an embedded system.

scholarly journals Efficient Lossless Compression of Multitemporal Hyperspectral Image Data

2018 ◽  
Vol 4 (12) ◽  
pp. 142 ◽  
Author(s):  
Hongda Shen ◽  
Zhuocheng Jiang ◽  
W. Pan
Keyword(s):  
Data Compression ◽  
Hyperspectral Image ◽  
Image Data ◽  
Lossless Compression ◽  
Large Data ◽  
Hyperspectral Data ◽  
Compression Performance ◽  
Temporal Correlations ◽  
Sensing Applications ◽  
Data Volume

Hyperspectral imaging (HSI) technology has been used for various remote sensing applications due to its excellent capability of monitoring regions-of-interest over a period of time. However, the large data volume of four-dimensional multitemporal hyperspectral imagery demands massive data compression techniques. While conventional 3D hyperspectral data compression methods exploit only spatial and spectral correlations, we propose a simple yet effective predictive lossless compression algorithm that can achieve significant gains on compression efficiency, by also taking into account temporal correlations inherent in the multitemporal data. We present an information theoretic analysis to estimate potential compression performance gain with varying configurations of context vectors. Extensive simulation results demonstrate the effectiveness of the proposed algorithm. We also provide in-depth discussions on how to construct the context vectors in the prediction model for both multitemporal HSI and conventional 3D HSI data.

scholarly journals Second compression for pixelated images under edge-based compression algorithms: JPEG-LS as an example

2021 ◽  
pp. 1-9
Author(s):  
Kamal Al-Khayyat ◽  
Imad Al-Shaikhli ◽  
Mohamad Al-Hagery
Keyword(s):  
Data Compression ◽  
Compression Ratio ◽  
Lossless Compression ◽  
General Purpose ◽  
Compression Algorithm ◽  
Random Data ◽  
Data Set ◽  
Compression Algorithms ◽  
Edge Based

This paper details the examination of a particular case of data compression, where the compression algorithm removes the redundancy from data, which occurs when edge-based compression algorithms compress (previously compressed) pixelated images. The newly created redundancy can be removed using another round of compression. This work utilized the JPEG-LS as an example of an edge-based compression algorithm for compressing pixelated images. The output of this process was subjected to another round of compression using a more robust but slower compressor (PAQ8f). The compression ratio of the second compression was, on average,  18%, which is high for random data. The results of the second compression were superior to the lossy JPEG. Under the used data set, lossy JPEG needs to sacrifice  10% on average to realize nearly total lossless compression ratios of the two-successive compressions. To generalize the results, fast general-purpose compression algorithms (7z, bz2, and Gzip) were used too.

Lossless compression method for acoustic waveform data based on wavelet transform and bit-recombination mark coding

Geophysics ◽  
2013 ◽  
Vol 78 (5) ◽  
pp. V219-V228 ◽  
Author(s):  
Ming Cai ◽  
Wenxiao Qiao ◽  
Xiaodong Ju ◽  
Xiaohua Che ◽  
Yuhong Zhao
Keyword(s):  
Wavelet Transform ◽  
Data Compression ◽  
Lossless Compression ◽  
Data Type ◽  
Compression Method ◽  
Type Conversion ◽  
Waveform Data ◽  
Limited Bandwidth ◽  
Compression Performance ◽  
Band Limited

In well logging, large amounts of data need to be sent from downhole to the surface by means of a very band-limited telemetry system. The limited bandwidth usually results in prolonging of expensive rig time and/or the sacrifice of borehole information. Data compression techniques, to some extent, may relieve this problem. We deduced the adaptive (4, 4) lifting integer-to-integer wavelet transform formula and its inverse transform formula based on the basic principle of wavelet transform, and we explored an appropriate bit-recombination mark coding approach according to the characteristics of wavelet transform coefficients. Then a new lossless compression method for acoustic waveform data based on wavelet transform and bit-recombination mark coding was discovered. The compression method mainly consists of wavelet transform, data type conversion, bit-recombination, and mark coding, whereas the decompression method consists of mark decoding, bit-recovery, data type conversion, and inverse wavelet transform. The compression and decompression programs were developed according to the proposed method. Compression and decompression tests were then applied to field and synthetic acoustic logging waveform data, and the compression performance of our method and several other lossless compression methods were compared and analyzed. Test results validated the correctness of our method and demonstrated its advantages. The new method is potentially applicable to acoustic waveform data compression.

scholarly journals PENGEMBANGAN DAN ANALISIS KOMBINASI RUN LENGTH ENCODING DAN RELATIVE ENCODING UNTUK KOMPRESI CITRA

2016 ◽  
Vol 12 (2) ◽  
Author(s):  
Yosia Adi Jaya ◽  
Lukas Chrisantyo ◽  
Willy Sudiarto Raharjo
Keyword(s):  
Data Compression ◽  
Compression Ratio ◽  
Fast Algorithm ◽  
Data Transfer ◽  
Lossless Compression ◽  
Compression Algorithm ◽  
Storage Space ◽  
Run Length ◽  
Relative Value ◽  
Image Test

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.

An Image Lossless Compression Algorithm Based on 3-PVLC

2014 ◽  
Vol 39 (8) ◽  
pp. 1289-1294
Author(s):  
Jian GAO ◽  
Jun RAO ◽  
Rui-Peng SUN
Keyword(s):  
Lossless Compression ◽  
Compression Algorithm

scholarly journals Frequency Selective Auto-Encoder for Smart Meter Data Compression

Sensors ◽  
2021 ◽  
Vol 21 (4) ◽  
pp. 1521
Author(s):  
Jihoon Lee ◽  
Seungwook Yoon ◽  
Euiseok Hwang
Keyword(s):  
Data Compression ◽  
High Frequency ◽  
Spectral Properties ◽  
Situational Awareness ◽  
Time Windows ◽  
Low Frequency ◽  
Smart Meters ◽  
Compression Performance ◽  
Reconstruction Performance ◽  
Frequency Selective

With the development of the internet of things (IoT), the power grid has become intelligent using massive IoT sensors, such as smart meters. Generally, installed smart meters can collect large amounts of data to improve grid visibility and situational awareness. However, the limited storage and communication capacities can restrain their infrastructure in the IoT environment. To alleviate these problems, efficient and various compression techniques are required. Deep learning-based compression techniques such as auto-encoders (AEs) have recently been deployed for this purpose. However, the compression performance of the existing models can be limited when the spectral properties of high-frequency sampled power data are widely varying over time. This paper proposes an AE compression model, based on a frequency selection method, which improves the reconstruction quality while maintaining the compression ratio (CR). For efficient data compression, the proposed method selectively applies customized compression models, depending on the spectral properties of the corresponding time windows. The framework of the proposed method involves two primary steps: (i) division of the power data into a series of time windows with specified spectral properties (high-frequency, medium-frequency, and low-frequency dominance) and (ii) separate training and selective application of the AE models, which prepares them for the power data compression that best suits the characteristics of each frequency. In simulations on the Dutch residential energy dataset, the frequency-selective AE model shows significantly higher reconstruction performance than the existing model with the same CR. In addition, the proposed model reduces the computational complexity involved in the analysis of the learning process.

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