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International Image Processing, Applications and Systems Conference (IPAS)

作者： Jesse Redford Xingjie Li University of North Carolina Charlotte Charlotte NC USA

ISBN: (数字)9781728175744

ISBN: (纸本)9781728175751

This paper offers a new feature-oriented compression algorithm for flexible reduction of data redundancy commonly found in images and videos streams. Using a combination of image segmentation and face detection techniques as a preprocessing step, we derive a compression framework to adaptively treat `feature' and `ground' while balancing the total compression and quality of `feature' regions. We demonstrate the utility of a feature compliant compression algorithm (FC-SVD), a revised peak signal-to-noise ratio PSNR assessment, and a relative quality ratio to control artificial distortion. The goal of this investigation is to provide new contributions to image and video processing research via multi-scale resolution and the block-based adaptive singular value decomposition.

关键词： Image segmentation Image coding PSNR Training data Video compression compression algorithms Videos

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Sensor Data Stream on-line compression with Linearity-based Methods

Sensor Data Stream on-line Compression with Linearity-based ...

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International Conference on Smart Computing (SMARTCOMP)

作者： Olli Väänänen Timo Hämäläinen School of Technology JAMK University of Applied Sciences Jyväskylä Finland Faculty of Information Technology University of Jyväskylä Jyväskylä Finland

ISBN: (数字)9781728169972

ISBN: (纸本)9781728169989

The escalation of the Internet of Things applications has put on display the different sensor data processing methods. The sensor data compression is one of the fundamental methods to reduce the amount of data needed to transmit from the sensor node which is often battery powered and operates wirelessly. Reducing the amount of data in wireless transmission is an effective way to reduce overall energy consumption in wireless sensor nodes. The methods presented and tested are suitable for constrained sensor nodes with limited computational power and limited energy resources. The methods presented are compared with each other using compression ratio and inherent latency. Latency is an important parameter in on-line applications. The improved variation of the linear regression-based method called RT-LRbTC is tested and it has proved to be a potential method to be used in a wireless sensor node with a fixed and predictable latency. The compression efficiency of the compression algorithms is tested with real measurement data sets.

关键词： Wireless communication Wireless sensor networks Energy consumption Data compression Prediction algorithms Internet of Things compression algorithms

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Polynomial Based Representation for DNA Sequence compression and Search

Polynomial Based Representation for DNA Sequence Compression...

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IEEE Punecon

作者： Waqar Ahmad Khan Aftab Khan University of Engineering and Technology (UET) Peshawar Pakistan

ISBN: (数字)9781728196008

ISBN: (纸本)9781728196015

Data compression plays an important role in lesser expensive resource consumption. It is also cumbersome to send a huge amount of data through the network. The data compression techniques help in optimizing physical storage devices as well as it makes easy to transfer the compressed data or file faster through internet or network. Deoxyribonucleic acid or DNA is biological traits presents in human and almost all other living organisms. The DNA contains the genetic information of the living organisms. In this research, various compression algorithms for DNA sequences are studied and compared. The scheme proposed here for compression is the use of a burrows wheeler compression technique. Our proposed algorithm is the modification of the Burrows Wheeler compression Algorithm (BWCA). The lexicographical sorting of the matrix is replaced by the polynomial base representation of the bases of DNA. This method reduces the time for compression positively. Validation was performed on various datasets that concur the efficiency of the proposed scheme.

关键词： DNA Graphics processing units Matrices Organisms Mathematical model compression algorithms Sorting

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Map and Trie based compression Algorithm for Data Transmission

Map and Trie based Compression Algorithm for Data Transmissi...

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International Conference on Innovative Mechanisms for Industry Applications (ICIMIA)

作者： Suyash Ghuge National Institute of Technology Mangalore Karnataka India

ISBN: (数字)9781728141671

ISBN: (纸本)9781728141688

In-text compression algorithms, a set of symbols as alphabets are defined. These alphabets are the basic building blocks of the language. Traditionally 8-bit ASCII characters are chosen as alphabets. In this paper, an approach that chooses larger symbols as alphabets is proposed. For example, in the case of the English language, a set of words and special characters are chosen as alphabets. In the case of HTML documents, HTML tags are included along with alphabets from the English language described before. Better compression results can be achieved since words have larger byte size than individual ASCII characters but still get mapped to similar-sized encodings. Compressing the data to be transmitted across networks implies less usage of network bandwidth, faster transmission of data, sending several data files over a single transfer and faster reading and writing of data. Thus, the transmission of a compressed file is cost-effective and also saves time in communication. The results prove that the proposed approach gives better file size compression concerning the compression ratio when compared with state-of-the-art algorithms.

关键词： Data compression Dictionaries compression algorithms Bandwidth Channel coding Decoding

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EFFICIENT MEDICAL IMAGE compression BASED ON INTEGER WAVELET TRANSFORM

EFFICIENT MEDICAL IMAGE COMPRESSION BASED ON INTEGER WAVELET...

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International Conference on Biosignals, Images and Instrumentation (ICBSII)

作者： R. Krishnaswamy S. NirmalaDevi University college of engineering Ariyalur Tamilnadu India Anna University Chennai Tamilnadu India

ISBN: (数字)9781728110479

ISBN: (纸本)9781728110486

Every day the amount of medical image generated grows rapidly so there is a need for efficient image compression for storing and managing the medical images. In this work, a suitable image codec is proposed for medical images. During the past few years, frequency domain analyzes such as Discrete Cosine Transform and Discrete Wavelet Transform (DWT) have been widely used in the field of image compression due to their well localized property of its coefficients in both space and frequency domain. This research work also deals with image compression based on frequency domain transformation. As the medical images are vital for diagnosis, they require lossless compression to store them. However, the coefficients of DWT are real numbers; lossless compression cannot be achieved. The Lifting Wavelet Transform (LWT) is used in proposed system to overcome limitation of DWT.

关键词： Image coding Discrete wavelet transforms compression algorithms Image reconstruction Biomedical imaging

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LOSSY COMPRESSIVE SENSING BASED ON ONLINE DICTIONARY LEARNING

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COMPUTING AND INFORMATICS 2019年第1期38卷 151-172页

作者： Ulku, Irem Kizgut, Ersin Cankaya Univ Dept Elect & Elect Engn Eskisehir Yolu 29 Km TR-06790 Ankara Turkey Univ Politecn Valencia IUMPA E-46071 Valencia Spain

In this paper, a lossy compression of hyperspectral images is realized by using a novel online dictionary learning method in which three dimensional datasets can be compressed. This online dictionary learning method and blind compressive sensing (BCS) algorithm are combined in a hybrid lossy compression framework for the first time in the literature. According to the experimental results, BCS algorithm has the best compression performance when the compression bit rate is higher than or equal to 0.5 bps. Apart from observing rate-distortion performance, anomaly detection performance is also tested on the reconstructed images to measure the information preservation performance.

关键词： Hyperspectral imaging compression algorithms dictionary learning sparse coding

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An Offloading Architecture of Lossless compression Based on Smart NIC

An Offloading Architecture of Lossless Compression Based on ...

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Computer Engineering and Intelligent Control (ICCEIC) International Conference on

作者： Can Zhang Yaming Xu Hongliang Wang Wei Liu Qi Mu Wei Guo Inspur Electronic Information Industry Co. Ltd. Jinan China Inspur (Beijing) Electronic Information Industry Co. Ltd. Beijing China

ISBN: (数字)9781728185736

ISBN: (纸本)9781728185743

With the explosive growth of information, data transmission and compression are becoming more and more important. Most of the existing compression algorithms are implemented by software architecture, which results in the waste of CPU resources. By unloading the algorithm to hardware, it is beneficial to reasonably allocate resources and improve the processing efficiency of CPU. The smart Network Interface Cards (NIC) based on FPGA can not only complete the task of data unloading, but also have the function of sending the compressed data to the cloud storage device directly. It provides a new operating environment for the compression algorithm with efficient network transmission speed, and is able to reduce the system power consumption within a certain *** this paper, we design an FPGA-based smart NIC that has the ability to unload CPU's LZ4 compression algorithm to FPGA to improve the utilization rate of CPU. Experiments show that the architecture is able to successfully open up the data path, and the network speed is nearly 46 Gb/s. At the same time, it can be seen from the experiments that the time required to run the LZ4 compression algorithm on the CPU is proportional to the size of files. It shows that for large files, using a smart NIC for data offloading has strong practical significance.

关键词： Software architecture Computer architecture Telecommunication traffic Task analysis compression algorithms Field programmable gate arrays Testing

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Two-Level Data compression using Machine Learning in Time Series Database

Two-Level Data Compression using Machine Learning in Time Se...

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International Conference on Data Engineering

作者： Xinyang Yu Yanqing Peng Feifei Li Sheng Wang Xiaowei Shen Huijun Mai Yue Xie Alibaba Group

ISBN: (数字)9781728129037

ISBN: (纸本)9781728129044

The explosion of time series advances the development of time series databases. To reduce storage overhead in these systems, data compression is widely adopted. Most existing compression algorithms utilize the overall characteristics of the entire time series to achieve high compression ratio, but ignore local contexts around individual points. In this way, they are effective for certain data patterns, and may suffer inherent pattern changes in real-world time series. It is therefore strongly desired to have a compression method that can always achieve high compression ratio in the existence of pattern diversity. In this paper, we propose a two-level compression model that selects a proper compression scheme for each individual point, so that diverse patterns can be captured at a fine granularity. Based on this model, we design and implement AMMMO framework, where a set of control parameters is defined to distill and categorize data patterns. At the top level, we evaluate each sub-sequence to fill in these parameters, generating a set of compression scheme candidates (i.e., major mode selection). At the bottom level, we choose the best scheme from these candidates for each data point respectively (i.e., sub-mode selection). To effectively handle diverse data patterns, we introduce a reinforcement learning based approach to learn parameter values automatically. Our experimental evaluation shows that our approach improves compression ratio by up to 120% (with an average of 50%), compared to other time-series compression methods.

关键词： Time series analysis Aerospace electronics Data compression Data models Learning (artificial intelligence) compression algorithms Transforms

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A Binary Line Buffer Circuit Featuring Lossy Data compression at Fixed Maximum Data Rate

A Binary Line Buffer Circuit Featuring Lossy Data Compressio...

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IEEE International Symposium on Circuits and Systems (ISCAS)

作者： Ettore Napoli Davide De Caro Nicola Petra Antonio G. M. Strollo

ISBN: (数字)9781728133201

ISBN: (纸本)9781728133218

Video processing requires an increasing amount of buffered data. The paper proposes a multi-line buffer circuit that stores compressed data thus saving logic and power. The lossy compression algorithm provides the output stream with a fixed, decided by the user, delay from the input stream. Further, the amount of memory of the compressed buffer can be designed to trade off the correctness of the output with the logic resources footprint. The circuit is tested in a computer vision processing chain as a binary line buffer for the stream of foreground pixels. The paper also proposes a multi-line buffer circuit that integrates a morphological operation. The latter, when compared against the state of the art and against the proposed multi-line lossy buffer, allows larger saving of logic resources (up to 75%) and power (up to 85%), with reduced penalty on video quality.

关键词： Computer vision Data compression Streaming media Buffer storage compression algorithms Delays Memory management

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Temporal-correlation-based compressive channel estimation for universal filtered multicarrier system over fast-fading channels

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JOURNAL OF AMBIENT INTELLIGENCE AND HUMANIZED COMPUTING 2019年第5期10卷 1681-1692页

作者： Wang, Rong Cai, Jingye Yu, Xiang Jiang, Jianchun Univ Elect Sci & Technol China Chengdu 610054 Sichuan Peoples R China Chongqing Univ Posts & Telecommun Chongqing 400065 Peoples R China

The universal filtered multicarrier technique is a competitive candidate multicarrier modulation scheme for 5G communication systems. Conventional channel-estimation algorithms suffer from significant performance losses due to the large spread in the delay of multipath channels in high-speed scenarios. To address this problem, we here propose a low-complexity, partial priori information-sparsity adaptive matching pursuit (PPI-SAMP) algorithm. Unlike the conventional SAMP algorithm, the PPI-SAMP algorithm improves performance over fast-fading channels by adequately exploiting the sparse characteristics and temporal correlation of wireless channels. First, the PPI-SAMP algorithm averages the channel impulse responses (CIRs) of consecutive symbols over the coherence time for achieving the accuracy required for coarse channel estimation. Second, the improved SAMP algorithm acquires the accurate CIRs with low complexity based on the coarse CIR. Moreover, the MSE performance and recovery probability with varying sizes of IBI-free region indicate that the proposed PPI-SAMP algorithm offers a longer CIR for multipath interference and is more robust against larger multipath-channel delay than the conventional SAMP and CoSaMP algorithms. The proposed algorithm also estimates channels more accurately than conventional SAMP and CoSaMP algorithms despite having a complexity reduced by approximately 52% compared to conventional SAMP algorithm.

关键词： Channel estimation compression algorithms Time-varying channels 5G mobile communication

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