scholarly journals Impact of Complexity and Compression Ratio of Compression Method on Lifetime of Vision Sensor Node

2017 ◽  
Vol 23 (3) ◽  
Author(s):  
Khursheed Aurangzeb ◽  
Musaed Alhussein ◽  
Syed Irtaza Haider
Keyword(s):  
Compression Ratio ◽  
Sensor Node ◽  
Vision Sensor ◽  
Compression Method

scholarly journals 3D DCT Based Image Compression Method for the Medical Endoscopic Application

Sensors ◽  
2021 ◽  
Vol 21 (5) ◽  
pp. 1817
Author(s):  
Jiawen Xue ◽  
Li Yin ◽  
Zehua Lan ◽  
Mingzhu Long ◽  
Guolin Li ◽  
...  
Keyword(s):  
Image Compression ◽  
Compression Ratio ◽  
Signal To Noise Ratio ◽  
Blocking Artifacts ◽  
Compression Method ◽  
High Compression Ratio ◽  
Multiplication Operation ◽  
3D Data ◽  
Wireless Capsule

This paper proposes a novel 3D discrete cosine transform (DCT) based image compression method for medical endoscopic applications. Due to the high correlation among color components of wireless capsule endoscopy (WCE) images, the original 2D Bayer data pattern is reconstructed into a new 3D data pattern, and 3D DCT is adopted to compress the 3D data for high compression ratio and high quality. For the low computational complexity of 3D-DCT, an optimized 4-point DCT butterfly structure without multiplication operation is proposed. Due to the unique characteristics of the 3D data pattern, the quantization and zigzag scan are ameliorated. To further improve the visual quality of decompressed images, a frequency-domain filter is proposed to eliminate the blocking artifacts adaptively. Experiments show that our method attains an average compression ratio (CR) of 22.94:1 with the peak signal to noise ratio (PSNR) of 40.73 dB, which outperforms state-of-the-art methods.

scholarly journals Parameter space dimension reduction of an adaptive interpolator during multidimensional signal differential compression

2019 ◽  
pp. 23-30
Author(s):  
A I Maksimov ◽  
M V Gashnikov
Keyword(s):  
Dimension Reduction ◽  
Parameter Space ◽  
Compression Ratio ◽  
Space Dimension ◽  
Computational Experiments ◽  
Parameter Range ◽  
Compression Method ◽  
Multidimensional Signals ◽  
Multidimensional Signal ◽  
Adaptive Interpolator

We propose a new adaptive multidimensional signal interpolator for differential compression tasks. To increase the efficiency of interpolation, we optimize its parameters space by the minimum absolute interpolation error criterion. To reduce the complexity of interpolation optimization, we reduce the dimension of its parameter range. The correspondence between signal samples in a local neighbourhood is parameterized. Besides, we compare several methods for such parameterization. The developed adaptive interpolator is embedded in the differential compression method. Computational experiments on real multidimensional signals confirm that the use of the proposed interpolator can increase the compression ratio.

scholarly journals An Enhanced Run-Length Encoding Compression Method for Telemetry Data

2017 ◽  
Vol 24 (3) ◽  
pp. 551-562 ◽  
Author(s):  
Yanhu Shan ◽  
Yongfeng Ren ◽  
Guoyong Zhen ◽  
Kaiqun Wang
Keyword(s):  
Compression Ratio ◽  
High Capacity ◽  
Acquisition System ◽  
Compression Method ◽  
Telemetry Data ◽  
Vibration Signals ◽  
Run Length ◽  
Compression Performance ◽  
Error Factor ◽  
Method Show

AbstractThe telemetry data are essential in evaluating the performance of aircraft and diagnosing its failures. This work combines the oversampling technology with the run-length encoding compression algorithm with an error factor to further enhance the compression performance of telemetry data in a multichannel acquisition system. Compression of telemetry data is carried out with the use of FPGAs. In the experiments there are used pulse signals and vibration signals. The proposed method is compared with two existing methods. The experimental results indicate that the compression ratio, precision, and distortion degree of the telemetry data are improved significantly compared with those obtained by the existing methods. The implementation and measurement of the proposed telemetry data compression method show its effectiveness when used in a high-precision high-capacity multichannel acquisition system.

scholarly journals A Simple and Efficient IQ Data Compression Method Based on Latency, EVM, and Compression Ratio Analysis

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 117436-117447 ◽  
Author(s):  
Jewon Lee ◽  
Eunhee Hyun ◽  
Joon-Young Jung
Keyword(s):  
Data Compression ◽  
Compression Ratio ◽  
Ratio Analysis ◽  
Compression Method

Application of Texture in a Compressed Visual Simulation

2014 ◽  
Vol 644-650 ◽  
pp. 4261-4264
Author(s):  
Wei Zhang ◽  
Chun Hua Zhang ◽  
Jia Yue Ren
Keyword(s):  
Compression Ratio ◽  
Visual Simulation ◽  
Compression Method ◽  
Texture Memory

Texture compression technology is an effective way to improve the capacity of the texture without increasing the texture memory. This paper studies a texture compression method based on mipmap technology, details several texture compression formats, and describes the texture file of several formats generating and corresponding parameters adjustment. Practical results show that the texture compression method based on mipmap technology can not only achieve high texture compression ratio, but also cause no effect on the texture quality.

Multi-Channel ECG Signals Compression Algorithm Using Simultaneous Orthogonal Matching Pursuit

2012 ◽  
Vol 457-458 ◽  
pp. 1305-1309
Author(s):  
Yong Ting Li ◽  
Xiao Yan Chen ◽  
Yue Wen Liu
Keyword(s):  
Signal Processing ◽  
Compression Ratio ◽  
Matching Pursuit ◽  
Orthogonal Matching Pursuit ◽  
Compression Method ◽  
Petersburg Institute ◽  
Redundant Dictionary ◽  
Compression Performance ◽  
Ecg Signals ◽  
Ecg Compression

Sparse decompression is a new theory for signal processing, having the advantage in that the base (dictionary) used in this theory is over-complete, and can reflect the nature of signa1. So the sparse decompression of signal can get sparse representation, which is very important in data compression. In this paper, a novel ECG compression method for multi-channel ECG signals was introduced based on the Simultaneous Orthogonal Matching Pursuit (S-OMP). The proposed method decomposes multi-channel ECG signals simultaneously into different linear expansions of the same atoms that are selected from a redundant dictionary, which is constructed by Hermite fuctions and Gobar functions in order to the best match the characteristic of the ECG waveform. Compression performance has been tested using a subset of multi-channel ECG records from the St.-Petersburg Institute of Cardiological Technics database, the results demonstrate that much less atoms are selected to present signals and the compression ratio of Multi-channel ECG can achieve better performance in comparison to Simultaneous Matching Pursuit (SMP).

A novel FPGA-based wireless vision sensor node

2009 ◽  
Author(s):  
Chao Hu Zhiyong ◽  
Liu Yingzi Pan ◽  
Zhenxing Zeng ◽  
Max Q.-H. Meng
Keyword(s):  
Sensor Node ◽  
Vision Sensor

scholarly journals New Lossless Compression Method using Cyclic Reversible Low Contrast Mapping (CRLCM)

2016 ◽  
Vol 6 (6) ◽  
pp. 2836
Author(s):  
Hendra Mesra ◽  
Handayani Tjandrasa ◽  
Chastine Fatichah
Keyword(s):  
Compression Ratio ◽  
Image Data ◽  
Lossless Compression ◽  
Huffman Coding ◽  
Compression Method ◽  
Low Contrast ◽  
Coding Method

<p>In general, the compression method is developed to reduce the redundancy of data. This study uses a different approach to embed some bits of datum in image data into other datum using a Reversible Low Contrast Mapping (RLCM) transformation. Besides using the RLCM for embedding, this method also applies the properties of RLCM to compress the datum before it is embedded. In its algorithm, the proposed method engages Queue and Recursive Indexing. The algorithm encodes the data in a cyclic manner. In contrast to RLCM, the proposed method is a coding method as Huffman coding. This research uses publicly available image data to examine the proposed method. For all testing images, the proposed method has higher compression ratio than the Huffman coding.</p>

scholarly journals Performance Analysis in Web Based Data Uploading using LZ77 Compression and Chunking Method

2016 ◽  
Vol 1 (1) ◽  
Author(s):  
Hadary Mallafi
Keyword(s):  
Compression Ratio ◽  
Combination Method ◽  
Window Length ◽  
Compression Method ◽  
File Size ◽  
Compression Process ◽  
Web Based ◽  
Client Server ◽  
Compression Time ◽  
Ratio Data

One of the limitations in data uploading process is the maximum request length, besides that the data size that us transferred is also an issue because it influences the data sending cost. One of the way to cope with the problem of maximum request length is by downsizing the file size (chunking). Another way to do it is by enlarging the maximum reques length. Downsizing the file size can be done by chunking the files into a smaller size or by compressing it. In this paper, the author conducted a research about the file compression process that is done in client server using the technology of AJAX and Webservice. In addition to that, the file compression is combined with file chunking. In this research, the compression method that is used is dictionary based i.e. Lempel Ziv 77(LZ77). This compression is used since it can be performed in AJAX. The analysis that is made by the researcher about the compression ratio, data sending process speed, compression time, decompression time, the compression method capability in handling the maximum request length and the combination method of compression and chunking in uploading process.  The result of this research shows that compression method can handle the maximum requet length. Based on the experiment conducted, the relations between the compression ratio and window length is positively corelated. It means that the greater the window length is the more the compression ratio is.  Meanwhile, the relation between window length and uploading time is negatively linearly corelated. It means that the greater the window length is the faster the uploading time is. In addition, it can also be observed that the relation between the decompression and the file size is positively linearly correlated. It means that the greater the file size is the more time needed for decompression is.

scholarly journals An Efficient Lossless Compression Method for Periodic Signals Based on Adaptive Dictionary Predictive Coding

2020 ◽  
Vol 10 (14) ◽  
pp. 4918
Author(s):  
Shaofei Dai ◽  
Wenbo Liu ◽  
Zhengyi Wang ◽  
Kaiyu Li ◽  
Pengfei Zhu ◽  
...  
Keyword(s):  
Data Compression ◽  
Compression Ratio ◽  
Predictive Coding ◽  
Lossless Compression ◽  
Transformation Method ◽  
Transform Coding ◽  
Periodic Signal ◽  
Compression Method ◽  
Compression Efficiency ◽  
Periodic Signals

This paper reports on an efficient lossless compression method for periodic signals based on adaptive dictionary predictive coding. Some previous methods for data compression, such as difference pulse coding (DPCM), discrete cosine transform (DCT), lifting wavelet transform (LWT) and KL transform (KLT), lack a suitable transformation method to make these data less redundant and better compressed. A new predictive coding approach, basing on the adaptive dictionary, is proposed to improve the compression ratio of the periodic signal. The main criterion of lossless compression is the compression ratio (CR). In order to verify the effectiveness of the adaptive dictionary predictive coding for periodic signal compression, different transform coding technologies, including DPCM, 2-D DCT, and 2-D LWT, are compared. The results obtained prove that the adaptive dictionary predictive coding can effectively improve data compression efficiency compared with traditional transform coding technology.

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