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检索条件"任意字段=IEEE Data Compression Conference"

共 16713 条记录，以下是381-390 订阅

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compression-Aware Video Super-Resolution

Compression-Aware Video Super-Resolution

引用

ieee/CVF conference on Computer Vision and Pattern Recognition (CVPR)

作者： Wang, Yingwei Isobe, Takashi Jia, Xu Tao, Xin Lu, Huchuan Tai, Yu-Wing Dalian Univ Technol Dalian Peoples R China Xiaohongshu Inc Shanghai Peoples R China Kuaishou Technol Beijing Peoples R China Peng Cheng Lab Shenzhen Peoples R China Hong Kong Univ Sci & Technol Hong Kong Peoples R China

ISBN: (纸本)9798350301298

Videos stored on mobile devices or delivered on the Internet are usually in compressed format and are of various unknown compression parameters, but most video super-resolution (VSR) methods often assume ideal inputs resulting in large performance gap between experimental settings and real-world applications. In spite of a few pioneering works being proposed recently to super-resolve the compressed videos, they are not specially designed to deal with videos of various levels of compression. In this paper, we propose a novel and practical compression-aware video super-resolution model, which could adapt its video enhancement process to the estimated compression level. A compression encoder is designed to model compression levels of input frames, and a base VSR model is then conditioned on the implicitly computed representation by inserting compression-aware modules. In addition, we propose to further strengthen the VSR model by taking full advantage of meta data that is embedded naturally in compressed video streams in the procedure of information fusion. Extensive experiments are conducted to demonstrate the effectiveness and efficiency of the proposed method on compressed VSR benchmarks. The codes will be available at https://***/aprBlue/CAVSR

关键词： Low-level vision

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Time Series data Mining: A Unifying View 11

Time Series Data Mining: A Unifying View

引用

ieee 11th International conference on data Science and Advanced Analytics (DSAA)

作者： Keogh, Eamonn Univ Calif Riverside Riverside CA 92521 USA

ISBN: (纸本)9798350364941;9798350364958

Time series data are ubiquitous;large volumes of such data are routinely created in scientific, industrial, entertainment, medical and biological domains. Examples include ECG data, gait analysis, stock market quotes, machine health telemetry, search engine throughput volumes etc. What do we want to do with such time series? Everything! Classification, clustering, joins, anomaly detection, motif discovery, similarity search, visualization, summarization, compression, segmentation, rule discovery etc. Rather than a deep dive in just one of these subtopics, in this tutorial I will show a surprisingly small set of high-level representations, definitions, distance measures and primitives can be combined to solve the first 90 to 99.9% of the problems listed above. The tutorial will be illustrated with numerous real-world examples created just for this tutorial, including examples from robotics, wearables, medical telemetry, astronomy, and (especially) animal behavior. Moreover, all sample datasets and code snippets will be released so that the tutorial attendees (and later, readers) can first reproduce the results demonstrated, before attempting similar analysis on their data.

关键词： Time series

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Fast Algorithms for Scientific data compression 30

Fast Algorithms for Scientific Data Compression

引用

30th International conference on High Performance Computing, data, and Analytics (HiPC)

作者： Banerjee, Tania Lee, Jaemoon Choi, Jong Gong, Qian Chen, Jieyang Klasky, Scott Rangarajan, Anand Ranka, Sanjay Univ Florida Gainesville FL 32611 USA Oak Ridge Natl Lab Oak Ridge TN USA Univ Alabama Birmingham Birmingham AL USA

ISBN: (纸本)9798350383225

Many scientific simulations and experiments generate terabytes to petabytes of data daily, necessitating data compression techniques. Unlike video and image compression, scientists require methods that accurately preserve primary data (PD) and derived quantities of interest (QoIs). In our previous work, we demonstrated the effectiveness of hybrid compression techniques that combine machine learning with traditional approaches. This paper presents innovative computational techniques aimed at expediting the compression pipeline. Our experiments, conducted on two distinct platforms with a large-scale XGC-based fusion simulation, demonstrate that the overhead incurred by these new approaches is less than one percent of the computational resources needed for the simulation.

关键词： data compression Machine learning High-performance computing

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Exploring the Effectiveness of Deep Learning in Audio compression and Restoration 2

Exploring the Effectiveness of Deep Learning in Audio Compre...

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2nd ieee International conference on Trends in Quantum Computing and Emerging Business Technologies, TQCEBT 2024

作者： Pushparani, S. Rekha, K. Sashi Sivagami, V.M. Usharani, R. Jothi, M. Meenakshi College of Engineering Department of Computer Science and Engineering Tamil Nadu Chennai India Saveetha Institute of Medical and Technical Sciences Saveetha School of Engineering Department of Computer Science and Engineering Tamil Nadu Chennai India Sri Venkateswara College of Engineering Department of Information Technology SriperumbudurTamil Nadu Chennai India Srm Institute of Science and Technology Faculty of Engineering and Technology Department of Computational Intelligence School of Computing Tamil Nadu Kattankulathur India Care College of Engineering Department of Artificial Intelligence & Data Science Tamil Nadu Trichy India

ISBN: (纸本)9798350384277

Modern audio processing requires compression and restoration for streaming services and data storage and transmission. This study examines how deep learning can address these difficulties and includes an architecture flow diagram. The suggested complex design has numerous integral stages optimized for audio compression and restoration. The input layer contains raw audio data. The pre-processing module normalizes and extracts features from raw data, frequently using Mel-frequency cepstral coefficients (MFCC) to capture audio characteristics. This module improves data quality by reducing noise and filtering. The architecture's heart is the compression module, where deep learning is used. This module compresses audio data using neural networks, maybe CNNs or RNNs. This compression is quantified by compression ratio and bitrate, with hypothetical values representing audio quality trade-offs. The compression module compacts audio data for storage or transmission. To minimize data size, quantization and encoding are common here. To accurately decompress audio, the decompression module mimics the compression module's architecture. Depending on needs, error correction or quality enhancement layers may be added. Filtering, equalization, and dynamic range compression may be used in the post-processing module to improve audio quality. If needed, a restoration module uses deep learning models to minimize noise and artifacts. This study presents four hypothetical scenarios to demonstrate different results. The scenarios emphasize high compression, balanced approaches, quality prioritization, and low resource utilization. Deep learning approaches can adapt to varied audio processing needs by balancing compression ratio, bitrate, audio quality (measured by PSNR), and restoration accuracy in each case. © 2024 ieee.

关键词： Audio streaming

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Online and Scalable data compression Pipeline with Guarantees on Quantities of Interest 19

Online and Scalable Data Compression Pipeline with Guarantee...

引用

19th ieee International conference on e-Science, e-Science 2023

作者： Banerjee, Tania Lee, Jaemoon Choi, Jong Gong, Qian Chen, Jieyang Chang, Choongseok Klasky, Scott Rangarajan, Anand Ranka, Sanjay University of Florida United States Oak Ridge National Laboratory United States University of Alabama Birmingham United States Princeton Plasma Physics Laboratory United States

ISBN: (纸本)9798350322231

data compression is becoming critical for data-intensive scientific applications. Scientists require compression techniques that accurately preserve derived quantities of interest (QoIs). Prior work has shown that a pipeline can be built to guarantee error on the primary data (PD) within user-defined bounds and achieve near-floating point QoI errors. In this paper, we present novel computational approaches for accelerating the pipeline and demonstrate results that enable concurrent execution of compression in parallel with the simulation nodes. This allows compression, including the writing of the required compression data, for the previous time step to be completed while the simulation proceeds with the current time step. Overall, the approach presented in this paper results in a 6-8 times improvement in computational overhead compared to previous work. These results were obtained using data generated by a large-scale fusion code called XGC, which produces hundreds of terabytes of data in a single day. © 2023 ieee.

关键词： data compression

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ImplicitTerrain: a Continuous Surface Model for Terrain data Analysis

ImplicitTerrain: a Continuous Surface Model for Terrain Data...

引用

ieee/CVF conference on Computer Vision and Pattern Recognition (CVPR)

作者： Feng, Haoan Xu, Xin De Floriani, Leila Univ Maryland College Pk MD 20742 USA

ISBN: (纸本)9798350365474

Digital terrain models (DTMs) are pivotal in remote sensing, cartography, and landscape management, requiring accurate surface representation and topological information restoration. While topology analysis traditionally relies on smooth manifolds, the absence of an easy-to-use continuous surface model for a large terrain results in a preference for discrete meshes. Structural representation based on topology provides a succinct surface description, laying the foundation for many terrain analysis applications. However, on discrete meshes, numerical issues emerge, and complex algorithms are designed to handle them. This paper brings the context of terrain data analysis back to the continuous world and introduces ImplicitTerrain1, an implicit neural representation (INR) approach for modeling high-resolution terrain continuously and differentiably. Our comprehensive experiments demonstrate superior surface fitting accuracy, effective topological feature retrieval, and various topographical feature extraction that are implemented over this compact representation in parallel. To our knowledge, ImplicitTerrain pioneers a feasible continuous terrain surface modeling pipeline that provides a new research avenue for our community.

关键词： Discrete Morse Theory Forman Gradient Geospatial data compression Implicit Neural Representation Morse Theory Terrain data Analysis Terrain Surface Topological Analysis

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Next-Generation Sequencing data compression and Storage Solutions

Next-Generation Sequencing Data Compression and Storage Solu...

引用

2024 ieee International conference on Communication, Computing and Signal Processing, IICCCS 2024

作者： Pandey, Sandeep Arora, Jejji Singh Al-Attabi, Kassem Sunnatilla Qizi, Ikramova Madina Sathi, G. Urmila, Uk Ies College of Technology Department of Electrical & Electronics Engineering M.P. Bhopal India Chandigarh Group of Colleges Chandigarh Engineering College Department of Computer Application Punjab Jhanjeri Mohali140307 India The Islamic University College Of Technical Engineering Department Of Computers Techniques Engineering Najaf Iraq The Islamic University Of Al Diwaniyah College Of Technical Engineering Department Of Computers Techniques Engineering Al Diwaniyah Iraq National Research University Tashkent Institute of Irrigation and Agricultural Mechanization Engineers" Tashkent Uzbekistan Prince Shri Venkateshwara Padmavathy Engineering College Chennai India Saveetha Institute of Medical and Technical Sciences Department of Ai & Ml Tamilnadu Chennai India

ISBN: (纸本)9798350390759

They produce huge amount of data which require efficient compression and management mechanisms hence the need to adopt Next-Generation Sequencing (NGS). In this paper, the performance of different compression algorithms and their implementation with local and cloud-based storage systems are assessed and compared using the lossless and lossy storage efficiency analysis. This study reveals that reference-based compression shows the highest compression ratio on the whole with lossy methods having shorter processing time ahead of other methods. A comparison of the costs shows the feasibility of local and cloud storage options to bear substantial costs. The study further supports that sophisticated algorithms as well as universally recognized formats are crucial for organizing NGS data Stream. © 2024 ieee.

关键词： Cloud storage

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PhD Forum: Distributed Radiance Fields for Edge Video compression and Metaverse Integration in Autonomous Driving 10

PhD Forum: Distributed Radiance Fields for Edge Video Compre...

引用

International conference on Smart Computing (SMARTCOMP)

作者： Dopiriak, Matus Tech Univ Kosice Dept Comp & Informat Kosice Slovakia

ISBN: (纸本)9798350349955;9798350349948

The metaverse is a virtual space that combines physical and digital elements, creating immersive and connected digital worlds. For autonomous mobility, it enables new possibilities with edge computing and digital twins (DTs) that offer virtual prototyping, prediction, and more. DTs can be created with 3D scene reconstruction methods that capture the real world's geometry, appearance, and dynamics. However, sending data for real-time DT updates in the metaverse, such as camera images and videos from connected autonomous vehicles (CAVs) to edge servers, can increase network congestion, costs, and latency, affecting metaverse services. Herein, a new method is proposed based on distributed radiance fields (RFs), multi-access edge computing (MEC) network for video compression and metaverse DT updates. RE-based encoder and decoder are used to create and restore representations of camera images. The method is evaluated on a dataset of camera images from the CARLA simulator. data savings of up to 75% were achieved for 11.264 I-frame - P-frame pairs by using RFs instead of I-frames, while maintaining high peak signal-to-noise ratio (PSNR) and structural similarity index measure (SSIM) qualitative metrics for the reconstructed images. Possible uses and challenges for the metaverse and autonomous mobility are also discussed.

关键词： autonomous driving digital twin edge computing metaverse radiance fields video compression

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A Target Region Based Non-Uniform Remote Sensing Image Process Method Using data Hiding 6

A Target Region Based Non-Uniform Remote Sensing Image Proce...

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6th International conference on Electronic Engineering and Informatics, EEI 2024

作者： Wei, Zhen Hui, Zheng Li, Mindong Yang, Guowei Xi'an Institute of Space Radio Technology Xi'an China

ISBN: (纸本)9798350353594

In this paper, by taking advantage of various image data hiding scheme, we proposed a non-uniform optical remote sensing image scheme of which informative target region remains lossless via the compression-reconstruction process. Two different strategies are proposed in case of different input image's characteristic itself. Experimental results show that over 30% of test image set fit the proposed scheme in which their target region can be lossless transmitted through digital image compression of compression ratio (CR) equals 4 or 16. Meanwhile, the vision quality of constructed background area is fairly acceptable. © 2024 ieee.

关键词： data hiding non-uniform process remote sesing imge target region

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Distributed Photovoltaic data compression Method Based on Time Series Prediction Model 4

Distributed Photovoltaic Data Compression Method Based on Ti...

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4th International conference on New Energy and Power Engineering, ICNEPE 2024

作者： Li, Bo Shi, Zhan Deng, Xiaozhi Wu, Zhentian Yang, Zhihua Electric Power Dispatching Control Center Guangdong Power Grid Co. Guangzhou China Guangdong Electric Power Communication Technology Co. Guangzhou China

ISBN: (纸本)9798331516086

With the proliferation of distributed photovoltaic (PV) systems, the need for efficient real-time monitoring and data management has become indispensable. This paper presents a novel approach that integrates the ProbAttention mechanism into the Transformer model to enhance predictive encoding compression. By optimizing the attention mechanism, this study addresses challenges related to time complexity, memory usage, and prediction errors, thereby significantly improving compression rates. A layered sampling method is employed to optimize the PV data for the encoder-decoder architecture. The ProbAttention-Transformer model, utilized as the predictor, demonstrates superior prediction accuracy and reduced prediction errors, requiring fewer bits for encoding and achieving higher compression ratios. Extensive experiments on datasets from PV meters and inverters corroborate the effectiveness of this approach. Comparative experiments reveal that the ProbAttention-Transformer model outperforms logTrans and LSTMa, reducing MSE scores by 10.9% and 23.8%, respectively, while improving compression ratios by 20.18% and 27.78%. © 2024 ieee.

关键词： data compression Distributed photovoltaic Predictive encoding Time series

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