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MOST: model-based compression with Outlier Storage for Time Series Data

Proceedings of the ACM on Management of Data 2023年第4期1卷 1-29页

作者： Zehai Yang Shimin Chen Institute of Computing Technology CAS & University of Chinese Academy of Sciences Beijing China

Time series data are used in a wide variety of applications. The explosive growth of the amount of time series data poses a significant challenge in efficient data storage and query processing. Unfortunately, existing compression techniques either show only low to medium compression ratio on time series data, or incur significant decompression overhead during query *** propose a novel compression technique, MOST (model-based compression with Outlier STorage) for time series data. As measurement values often change smoothly in a period of time, we divide a time series into segments of smooth changes, then compute a linear model for each segment. Since tiny errors are often acceptable in analysis tasks, we omit data points whose computed values are within a pre-specified error threshold from the actual values, thereby effectively reducing the data size. Outliers are rare but important for many applications, and therefore we store outliers explicitly. Moreover, for processing MOST compressed data, we propose a segment-outlier dual-mode query engine that computes segments as a whole as much as possible, and build a prototype MostDB. Experimental results on real-world data sets show that MOST achieves 9.45-15.04x compression ratios. Compared to existing time series databases, MostDB achieves up to 11.68x speedups for common queries from the IoTDB Benchmark.

关键词： dual-mode query engine model-based compression outlier

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A Multitone model-based Seismic Data compression

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IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS 2022年第2期52卷 1030-1040页

作者： Liu, Bo Mohandes, Mohamed Nuha, Hilal Deriche, Mohamed Fekri, Faramarz McClellan, James H. King Fahd Univ Petr & Minerals Dept Elect Engn Dhahran 31261 Saudi Arabia Georgia Inst Technol Sch Elect & Comp Engn Atlanta GA 30332 USA

This work develops a model-based compression scheme for seismic data. First, seismic traces are modeled as multitone sinusoidal waves superposition. Each sinusoidal wave is regarded as a model component and is represented by a set of distinct parameters. Second, a parameter estimation algorithm for this model is proposed accordingly. In this algorithm, the parameters are estimated for each component sequentially. A suitable number of model components is determined by the level of the residuals energy. Next, the residuals are compressed using entropy coding or quantization coding techniques. The corresponding compression ratios are presented. Finally, the proposed model-based compression scheme is compared with the linear predictive coding (LPC) algorithm and the distributed principal component analysis (DPCA) algorithm on a real seismic database. The performance of the proposed model based is shown to be superior to that of the LPC and DPCA.

关键词： Data models Parameter estimation Transforms Encoding Analytical models Redundancy Optimization Data compression model-based compression parameter estimation seismic traces sinusoidal waves

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model Optimization for model-based compression of Real World Video Data 4

Model Optimization for Model-Based Compression of Real World...

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4th IEEE International Conference on Consumer Electronics Berlin (ICCE-Berlin)

作者： Feller, Christian Wuenschmann, Juergen Wagner, Raimar Rothermel, Albrecht Univ Ulm Inst Microelect Albert Einstein Allee 43 D-89081 Ulm Germany

ISBN: (纸本)9781479961658

The availability of affordable cameras with additional depth sensor boosted the advances in the field of Simultaneous Localization and Mapping (SLAM). This area of research addresses creating a 3D representation of unknown environment by fusing sensor information. Whereas a coarse representation suffices for navigational purposes, the resulting 3D models are not accurate enough to allow rendering a visually pleasing video stream based on the gathered information, when applying a model based video compression standard such as MPEG-4 Part 25. This publication focuses on increasing the accuracy of the created 3D models in order to improve the perceived quality when using model-based video coding and aims for performance comparable to hybrid video encoding.

关键词： cameras data compression image fusion video coding 3D models 3D representation MPEG-4 Part 25 SLAM coarse representation depth sensor model based video compression standard model optimization model-based compression model-based video coding navigational purposes perceived quality improvement real world video data sensor information fusion simultaneous localization-and-mapping unknown environment visually pleasing video stream rendering Accuracy Cameras Data models Equations Mathematical model Solid modeling Three-dimensional displays

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Seismic data modeling and compression using particle swarm optimization

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ARABIAN JOURNAL OF GEOSCIENCES 2021年第23期14卷 1-11页

作者： Nuha, Hilal Liu, Bo Mohandes, Mohamed Balghonaim, Adil Fekri, Faramarz King Fahd Univ Petr & Minerals Ctr Energy & Geoproc Dhahran 31261 Saudi Arabia Telkom Univ HUMIC Engn Bandung Indonesia Georgia Inst Technol Sch Elect & Comp Engn Atlanta GA 30332 USA

The modern seismic acquisition produces a large data volume that significantly increases the cost of storage and transmission. Therefore, it is desirable to reduce the cost by compressing the seismic data. In this work, we propose a model-based compression scheme to deal with the large data volume. First, each seismic trace is modeled as a superposition of multiple exponential decaying sinusoidal waves (EDSWs). Each EDSW represents a model component and is determined by a set of parameters. Secondly, a single component parameter estimation algorithm is proposed accordingly using the particle swarm optimization (PSO) technique. We extend the algorithm for multiple model components by sequentially estimating the parameter set wave by wave. A suitable number of model components for each trace is selected according to the level of the energy of residuals. Next, the residuals are encoded using quantization coding techniques to improve the reconstruction quality. Comparison of PSO, genetic algorithm (GA), and simulated annealing (SA) is presented where the proposed model estimation technique outperforms the comparative models. The proposed model-based compression scheme is experimentally compared with the discrete cosine transform (DCT) on a real seismic dataset. The performance of the proposed model-based exhibits superiority to that of the DCT.

关键词： model-based compression Seismic data Particle swarm optimization Discrete cosine transform

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ULTRA-LOW BITRATE VIDEO CONFERENCING USING DEEP IMAGE ANIMATION 29

ULTRA-LOW BITRATE VIDEO CONFERENCING USING DEEP IMAGE ANIMAT...

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IEEE International Conference on Image Processing (ICIP)

作者： Konuko, Goluck Valenzise, Giuseppe Lathuiliere, Stephane Tecom Paris Inst polytechn Paris LTCI Paris France Univ Paris Saclay CNRS CentraleSupelec Lab Signaux & Syst Paris France

ISBN: (数字)9781665496209

ISBN: (纸本)9781665496209

In this work we propose a novel deep learning approach for ultra-low bitrate video compression for video conferencing applications. To address the shortcomings of current video compression paradigms when the available bandwidth is extremely limited, we adopt a model-based approach that employs deep neural networks to encode motion information as keypoint displacement and reconstruct the video signal at the decoder side. The overall system is trained in an end-to-end fashion minimizing a reconstruction error on the encoder output. Objective and subjective quality evaluation experiments demonstrate that the proposed approach provides an average bitrate reduction for the same visual quality of more than 60% compared to HEVC.

关键词： Video compression Video conferencing model-based compression Deep learning

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ULTRA-LOW BITRATE VIDEO CONFERENCING USING DEEP IMAGE ANIMATION

ULTRA-LOW BITRATE VIDEO CONFERENCING USING DEEP IMAGE ANIMAT...

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IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

作者： Konuko, Goluck Valenzise, Giuseppe Lathuiliere, Stephane Inst Polytech Paris Telecom Paris LTCI Paris France Univ Paris Saclay CNRS Cent Supelec Lab Signaux & Syst Paris France

ISBN: (纸本)9781728176055

关键词： Video compression Video conferencing model-based compression Deep learning

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Extreme compression of fingerprint image databases using the model-based transform

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SIGNAL IMAGE AND VIDEO PROCESSING 2017年第8期11卷 1543-1550页

作者： Mansouri, Hamid Pourreza, Hamidreza Ferdowsi Univ Mashhad Dept Comp Engn Machine Vis Lab Mashhad Iran

In this paper, a model-based compression algorithm is proposed to compress the fingerprint image data. The proposed model is based on parallel lines with identical orientations, arbitrary widths, and similar gray-level values positioned on a rectangle with a constant gray-level value as the background. This algorithm is referred to as Parallel Stroked Multi Line (PSML). A compression algorithm is developed based on Adaptive Geometrical Wavelet which employs PSML to preserve the fingerprint structure and minutiae. The proposed PSML algorithm has significant advantages over Wedgelets Transform and JPEG2000 in terms of PSNR value, visual quality of the compressed images, and compression effect on Automatic Fingerprint Identification Systems (AFIS). On the ***.U 400 database, the mean EER rate for uncompressed images is 4.54%, whereas at extreme compression rates, like 267:1, this value is 49.41% and 6.22% for JPEG2000 and PSML, respectively. The results show a significant improvement compared to the standard JPEG2000 algorithm.

关键词： Parallel Stroked Multi Line Adaptive Geo-metrical Wavelet model-based compression Fingerprint image compression Large fingerprint database

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Demonstration of modelarDB: model-based Management of Dimensional Time Series 19

Demonstration of ModelarDB: Model-Based Management of Dimens...

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ACM SIGMOD International Conference on Management of Data (SIGMOD)

作者： Jensen, Soren Kejser Pedersen, Torben Bach Thomsen, Christian Aalborg Univ Aalborg Denmark

ISBN: (纸本)9781450356435

Due to the big amounts of sensor data produced, it is infeasible to store all of the data points collected and practitioners currently hide outliers by storing simple aggregates instead. As a remedy, we demonstrate modelarDB, a model-based Time Series Management System (TSMS) for time series with dimensions and possibly gaps. In this demonstration, participants can ingest data sets from multiple domains and experience how modelarDB provides fast ingestion and a high compression ratio by adaptively compressing time series using a set of models to accommodate changes in the structure of each time series over time. models approximate time series within a user-defined error bound (possibly zero). Participants can also experience how the compression ratio can be improved by ingesting correlated time series in groups created by modelarDB from user-hints. Participants provide these using primitives for describing correlation. Last, participants can execute SQL queries on the ingested data sets and see how the system optimizes queries directly on models.

关键词： model-based query processing olap sensor correlation time series distribution model-based compression model-based data management

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Multi-camera tele-immersion system with real-time model driven data compression

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VISUAL COMPUTER 2010年第1期26卷 3-15页

作者： Lien, Jyh-Ming Kurillo, Gregorij Bajcsy, Ruzena George Mason Univ Fairfax VA 22030 USA Univ Calif Berkeley Berkeley CA 94720 USA

Vision-based full-body 3D reconstruction for tele-immersive applications generates large amount of data points, which have to be sent through the network in real time. In this paper, we introduce a skeleton-based compression method using motion estimation where kinematic parameters of the human body are extracted from the point cloud data in each frame. First we address the issues regarding the data capturing and transfer to a remote site for the tele-immersive collaboration. We compare the results of the existing compression methods and the proposed skeleton-based compression technique. We examine the robustness and efficiency of the algorithm through experimental results with our multi-camera tele-immersion system. The proposed skeleton-based method provides high and flexible compression ratios from 50:1 to 5000:1 with reasonable reconstruction quality (peak signal-to-noise ratio from 28 to 31 dB) while preserving real-time (10+ fps) processing.

关键词： 3D tele-immersion Multi-camera tele-immersion system Point data compression model-based compression

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Fully automatic perceptual modeling of near regular textures

Fully automatic perceptual modeling of near regular textures

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Conference on Human Vision and Electronic Imaging XII

作者： Menegaz, G. Franceschetti, A. Mecocci, A. Univ Siena Dept Informat Engn Via Laterina 8 I-53100 Siena Italy

ISBN: (纸本)9780819466051

Near regular textures feature a relatively high degree of regularity. They can be conveniently modeled by the combination of a suitable set of textons and a placement rule. The main issues in this respect are the selection of the minimum set of textons bringing the variability of the basic patterns;the identification and positioning of the generating lattice;and the modelization of the variability in both the texton structure and the deviation from periodicity of the lattice capturing the naturalness of the considered texture. In this contribution, we provide a fully automatic solution to both the analysis and the synthesis issues leading to the generation of textures samples that are perceptually indistinguishable from the original ones. The definition of an ad-hoe periodicity index allows to predict the suitability of the model for a given texture. The model is validated through psycho-visual experiments providing the conditions for subjective equivalence among the original and synthetic textures, while allowing to determine the minimum number of textons to be used to meet such a requirement for a given texture class. This is of prime importance in model-based coding applications, as is the one we foresee, as it allows to minimize the amount of information to be transmitted to the receiver.

关键词： near regular textures texture perception model-based compression

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