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

共 16713 条记录，以下是241-250 订阅

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Feature compression for Multimodal Multi-Object Tracking

Feature Compression for Multimodal Multi-Object Tracking

引用

41st ieee Military Communications conference (ieee MILCOM) - Communications Supporting Military Operations in a Contested Environment

作者： Li, Xinlin Hanna, Osama A. Fragouli, Christina Diggavi, Suhas Verma, Gunjan Bhattacharyya, Joydeep Univ Calif Los Angeles Elect & Comp Engn Los Angeles CA 90024 USA DEVCOM Army Res Lab Adelphi MD USA

ISBN: (纸本)9798350321814

Distributed multi-sensor systems that collect multimodal data can serve as a rich source of information and provide broad perception to support applications. However, challenges arise when communication channels grapple with bandwidth limitations. A recent work [1] introduced a novel deep compression scheme that exhibits state-of-the-art performance in unimodal classification tasks. Our work builds on this compression scheme and explores its performance on more sophisticated multimodal applications. In particular, we showcase its performance in an object-tracking system through experiments with realistic settings, where we show that more than 99% savings in communication cost can be achieved with very small loss in tracking accuracy.

关键词： Multimodal data compression Tracking

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Lossy compression of Point Clouds Using Coding Rate Reduction Compressed Sparse Representation 5

Lossy Compression of Point Clouds Using Coding Rate Reductio...

引用

5th ieee International conference on Power, Electronics and Computer Applications, ICPECA 2025

作者： Li, Yinghao Wang, Yanjie Wang, Wenmeng Li, Yafei Yao, Mengyuan Li, Jingke School of Cyberspace Security Zhengzhou University Henan Zhengzhou China Henan Technical College of Construction Henan Zhengzhou China

ISBN: (纸本)9798331533694

Point cloud compression is a technique that aims to address the challenge of storing and transmitting large-scale 3D data by reducing the size of point cloud data while maintaining sufficient useful information. However, the large amount of data in point clouds and their irregular spatial arrangement complicates storage and transmission. To address this issue, a novel lossy point cloud compression method is proposed in this paper. Our strategy treats point cloud compression as an iterative optimization process, where each layer performs an optimization step to achieve the sparse compression goal. An autoencoder structure is used to enhance the point cloud compression task with coding rate reduction transformer and geometric attribute information. In the encoding phase, a novel self-encoder is used to learn the compressed and sparse representation and geometric features of the point cloud to be used as a compressed embedding of the point cloud. In the decoding stage, point-wise split deconvolution is designed for upsampling the point cloud in order to better reconstruct the point cloud. Experimental results show that the proposed method achieves better reconstruction quality and geometry at the same bit rate as other state-of-the-art compression methods. © 2025 ieee.

关键词： data compression

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Appliances Load Pattern Reconstruction from Adaptive Delta-Driven Sampled Smart Meter data

Appliances Load Pattern Reconstruction from Adaptive Delta-D...

引用

ieee International Instrumentation and Measurement Technology conference (I2MTC)

作者： Qaisar, Saeed Mian Lopez, Alberto Kitanneh, Omar Ferrero, Francisco Effat Univ Elect & Comp Engn Dept Jeddah 21478 Saudi Arabia Univ Oviedo Dept Elect Elect Commun & Syst Engn Gijon 33204 Spain Effat Univ Dept Nat Sci Math & Technol Jeddah 22332 Saudi Arabia

ISBN: (纸本)9798350380903;9798350380910

In recent days the interest in the usage of smart meters is raising. It is evident from the widespread use of smart meters in contemporary society. The stakeholders in the smart grid must profit from the gathering and processing of fine-grained metering data. Time invariance characterizes the classical data sampling method. As a result, a significant volume of unnecessary data is gathered, sent, and analyzed. A method of adaptive delta-driven sampling (ADDS) of the smart meter data is proposed. It compensates the aforementioned shortfall and can lead towards a significant real-time compression without losing pertinent information. Subsequently, the compressed form of data can be efficiently processed, analyzed, stored and transmitted. It promises a significant transmission and computational effectiveness with a diminished latency. It is shown that the devised form of compressed data can be effectively reconstructed using a low complexity reconstruction algorithm. The reconstruction error is measured in terms of the root mean square error (RMSE) and the mean absolute error (MAE). The applicability is tested using the power consumption patterns of coffee machines, computer stations, fridges and freezers. The proposed solution attains an overall compression gain of 1.84-times, 2.49-times, 7.55-times respectively for the coffee machine, computer stations, and fridges and freezers. Moreover, the obtained values of RMSE and MAE confirm an appropriate reconstruction using the devised method.

关键词： Smart meter data Adaptive Delta-Driven sampling Reconstruction Reconstruction error

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Enabling Encrypted Delta compression for Outsourced Storage Systems via Preserving Similarity 41

Enabling Encrypted Delta Compression for Outsourced Storage ...

引用

41st ieee International conference on Computer Design (ICCD)

作者： Gan, Chuang Hu, Yuchong Zhao, Leyan Zhao, Xin Gong, Pengyu Zhang, Wenhao Wang, Lin Feng, Dan Huazhong Univ Sci & Technol Wuhan Peoples R China

ISBN: (纸本)9798350342918

Outsourcing storage to the cloud can relieve pressure on storage capacity, wherein encrypted deduplication is often used to perform deduplication on encrypted data, for ensuring both storage efficiency and security. However, encrypted deduplication only removes duplicate encrypted data without considering non-duplicate but very similar ones. In this paper, we propose EDelta, which is the first to enable encrypted delta compression so as to further reduce redundancy of similar encrypted data atop encrypted deduplication, by means of preserving similarity after encryption. We also propose two methods to speed up the encryption process of encrypted delta compression. We prototype EDelta in cloud environments and our experimental results based on real-world datasets show that EDelta can achieve an additional data reduction ratio by up to 45.0% on top of encrypted deduplication.

关键词： data deduplication Delta compression Encrypted deduplication

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Comparison of Learned Image compression Methods and JPEG

Comparison of Learned Image Compression Methods and JPEG

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2024 Innovations in Intelligent Systems and Applications conference, ASYU 2024

作者： Öztürk, Emir Mesut, Altan Computer Engineering Department Faculty of Engineering Trakya University Edirne Turkey

ISBN: (纸本)9798350379433

Media such as audio, images, and videos, which occupy significant storage space in digital environments, are often compressed to save space, particularly outside professional settings. The compression process aims to achieve space savings while maintaining an acceptable level of quality. Many standard image compression algorithms are based on the principle of eliminating data that the human eye cannot perceive and selectively removing certain data after performing spatial transformation on the obtained image. In recent years, compression algorithms utilizing autoencoders or generative adversarial network (GAN) architectures have emerged. These algorithms fundamentally aim to reduce the dimensionality of data and create a representation of it. The dimension reduction stage used is considered equivalent to the compression process. In this study, the performance measurement of three different models using autoencoders and GANs is conducted, and the results are compared with the performance of the JPEG algorithm in terms of speeds and ratio. Upon examining the results, it is observed that learned image compression methods are catching up with JPEG in terms of quality. The methods achieve better results in terms of compression ratio compared to JPEG, but they operate much slower than JPEG in terms of processing time and have some artificial artifacts. © 2024 ieee.

关键词： Generative adversarial networks

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Power-Efficient Multimodal data Transmission via NOMA with Optimal SIC Ordering

Power-Efficient Multimodal Data Transmission via NOMA with O...

引用

ieee/CIC International conference on Communications in China (ICCC)

作者： Wang, Tianshun Li, Yang Feng, Panpan Gao, Yun Wei, Xin Ren, Qilei Nanjing Univ Posts & Telecommun Sch Commun & Informat Engn Nanjing 210003 Peoples R China Southwest Jiaotong Univ Sch Comp & Artificial Intelligence Chengdu Peoples R China

ISBN: (纸本)9798350378412

In the context of promoting intelligence and informatization, multimodal perception surveillance systems have become a promising solution for comprehensive environmental monitoring across different dimensions. However, these systems face key challenges in processing and transmitting heterogeneous data from various perceptrons, which requires effective resource allocation strategies. This paper addresses this challenge by integrating non-orthogonal multiple access (NOMA) into the communication framework of multimodal perception surveillance systems to improve the efficiency of multimodal data transmission. Specifically, this paper proposes a new search algorithm that combines binary space compression with cross entropy (CE) algorithm to optimize the efficient allocation of limited spectrum resources and the successive interference cancellation (SIC) ordering of the perceptrons. The proposed ordering compression-based CE (OCCE) algorithm treats the binary encoding of SIC ordering as a stochastic learning process, which significantly reduces computational complexity while ensuring the accuracy of the optimal solution. Meanwhile, we also obtained an iterative analytical formula for the optimal transmit-powers of the perceptrons through mathematical analysis. The numerical results verify the effectiveness of the proposed algorithm in optimizing the resource allocation of the multimodal perception surveillance system, as well as its superior performance in terms of accuracy and computational efficiency.

关键词： Non-orthogonal multiple access multimodal data transmission successive interference cancellation

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TINC: Tree-structured Implicit Neural compression

TINC: Tree-structured Implicit Neural Compression

引用

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

作者： Yang, Runzhao Tsinghua Univ Dept Automat Beijing 100084 Peoples R China

ISBN: (纸本)9798350301298

Implicit neural representation (INR) can describe the target scenes with high fidelity using a small number of parameters, and is emerging as a promising data compression technique. However, limited spectrum coverage is intrinsic to INR, and it is non-trivial to remove redundancy in diverse complex data effectively. Preliminary studies can only exploit either global or local correlation in the target data and thus of limited performance. In this paper, we propose a Tree-structured Implicit Neural compression (TINC) to conduct compact representation for local regions and extract the shared features of these local representations in a hierarchical manner. Specifically, we use Multi-Layer Perceptrons (MLPs) to fit the partitioned local regions, and these MLPs are organized in tree structure to share parameters according to the spatial distance. The parameter sharing scheme not only ensures the continuity between adjacent regions, but also jointly removes the local and non-local redundancy. Extensive experiments show that TINC improves the compression fidelity of INR, and has shown impressive compression capabilities over commercial tools and other deep learning based methods. Besides, the approach is of high flexibility and can be tailored for different data and parameter settings. The source code can be found at https://***/RichealYoung/TINC.

关键词： cell microscopy Medical and biological vision

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CoDISP: Exploring Compressed Domain Camera ISP with RGB-guided Encoder

CoDISP: Exploring Compressed Domain Camera ISP with RGB-guid...

引用

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

作者： Zhang, Molin Majee, Soumendu Wang, Chengyu Lee, Seok-Jun Sheikh, Hamid MIT Cambridge MA 02139 USA Samsung Res Amer Mountain View CA USA Samsung Res Amer MPI Lab Mountain View CA USA

ISBN: (纸本)9798350365474

Most mobile device Image Signal Processing (ISP) pipelines operate directly on RAW image data for all processing tasks. However, the rise of super-high-resolution cameras on mobile devices has led to increased memory demands for multi-frame ISP pipelines. In this work, we introduce a novel ISP pipeline that operates on a learned compressed domain, aiming to conserve memory for downstream ISP modules' inputs. We utilize RGB image compression to define a compressed latent domain, preserving both semantic information and high-frequency details. To facilitate mapping of raw images to the compressed domain, we develop a transfer learning strategy. All downstream processing tasks, including demosaicing, single and multi-frame denoising, and registration, are performed on this compressed latent domain. We demonstrate the effectiveness of our compressed domain ISP pipeline on both public and internal datasets. Remarkably, our pipeline achieves ISP performance similar to non-compression methods while significantly reducing mobile memory requirements.

关键词： Deep learning Image compression Mobile phone ISP Transfer learning

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CROSSWORD: A SEMANTIC APPROACH TO TEXT compression VIA MASKING 49

CROSSWORD: A SEMANTIC APPROACH TO TEXT COMPRESSION VIA MASKI...

引用

49th ieee International conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Li, Mingxiao Jin, Rui Xiang, Liyao Shen, Kaiming Cui, Shuguang Chinese Univ Hong Kong Shenzhen Sch Sci & Engn FNii Shenzhen Peoples R China Shanghai Jiao Tong Univ Shanghai Peoples R China

ISBN: (纸本)9798350344868;9798350344851

Conventional data compression methods typically model the information source as an i.i.d. stochastic process, thereby establishing the fundamental limit as entropy for lossless compression and as mutual information for lossy compression. However, the source in the real world (e.g., text, music, and speech) is often statistically ill-defined because of its close connection to human perception. This work aims to exploit the semantic aspect of text as inspired by the puzzle crossword. The main idea is to only compress those semantically important words while masking the rest;the proposed decompressor can recover all the missing words automatically according to context. Experiments show that the proposed semantic approach can achieve much higher compression efficiency than the state-of-the-art semantic compression method.

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Laplacian-guided Entropy Model in Neural Codec with Blur-dissipated Synthesis

Laplacian-guided Entropy Model in Neural Codec with Blur-dis...

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

作者： Khoshkhahtinat, Atefeh Zafari, Ali Mehta, Piyush M. Nasrabadi, Nasser M. West Virginia Univ Morgantown WV 26506 USA

ISBN: (纸本)9798350353013;9798350353006

While replacing Gaussian decoders with a conditional diffusion model enhances the perceptual quality of reconstructions in neural image compression, their lack of inductive bias for image data restricts their ability to achieve state-of-the-art perceptual levels. To address this limitation, we adopt a non-isotropic diffusion model at the decoder side. This model imposes an inductive bias aimed at distinguishing between frequency contents, thereby facilitating the generation of high-quality images. Moreover, our framework is equipped with a novel entropy model that accurately models the probability distribution of latent representation by exploiting spatio-channel correlations in latent space, while accelerating the entropy decoding step. This channel-wise entropy model leverages both local and global spatial contexts within each channel chunk. The global spatial context is built upon the Transformer, which is specifically designed for image compression tasks. The designed Transformer employs a Laplacian-shaped positional encoding, the learnable parameters of which are adaptively adjusted for each channel cluster. Our experiments demonstrate that our proposed framework yields better perceptual quality compared to cutting-edge generative-based codecs, and the proposed entropy model contributes to notable bitrate savings. The code is available at https://***/Atefeh-Khoshtinat/Blur-dissipated-compression.

关键词： Entropy Model Neural Image compression Non-isotropic Diffusion Model Transformer

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