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

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Entropy Modeling via Gaussian Process Regression for Learned Image compression

Entropy Modeling via Gaussian Process Regression for Learned...

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data compression conference (DCC)

作者： Cao, Maida Dai, Wenrui Li, Shaohui Li, Chenglin Zou, Junni Chen, Ying Xiong, Hongkai Shanghai Jiao Tong Univ Sch Elect Informat & Elect Engn Shanghai 20040 Peoples R China Alibaba Grp Hangzhou 310052 Peoples R China

ISBN: (纸本)9781665478939

Existing entropy models in learned image compression are cumbersome to generate fixed mean and variance for estimating Gaussian distributions for latent representation. In this paper, we propose a novel entropy model based on Gaussian process regression (GPR) that flexibly predicts the mean of Gaussians with posterior distributions characterized by covariance functions spanned in the high-dimensional feature space. Furthermore, we develop the rate-distortion optimization based on the proposed entropy model by approximating the bitrates with the evidence lower bound (ELBO) derived via variational inference for GPR. The proposed model can be seamlessly integrated into existing end-to-end optimized frameworks by substituting the masked convolution based autoregressive models. Experimental results demonstrate that the proposed model outperforms conventional image compression methods such as JPEG2000 and BPG, as well as recent learning based methods on the Kodak dataset in terms of rate-distortion performance.

关键词： Image coding Convolution Bit rate Rate-distortion Transform coding Gaussian processes Predictive models

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FedADP: Communication-Efficient by Model Pruning for Federated Learning

FedADP: Communication-Efficient by Model Pruning for Federat...

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ieee conference on Global Communications (ieee GLOBECOM) - Intelligent Communications for Shared Prosperity

作者： Liu, Haiyang Shi, Yuliang Su, Zhiyuan Zhang, Kun Wang, Xinjun Yan, Zhongmin Kong, Fanyu Shandong Univ Sch Software Jinan Peoples R China Jinan Inspur Data Technol Jinan Peoples R China Univ Jinan Sch Informat Sci & Engn Jinan Peoples R China

ISBN: (纸本)9798350310900

Federated learning is a new type of artificial intelligence technology. During the training process, the client transmits model parameter information instead of local data to ensure their privacy and security. But it also incurs higher communication costs. This article proposes a new federated learning pruning method, FedADP, with the aim of adaptively determining pruning ratios for each layer in each client model without infringing on client privacy, and achieving more accurate pruning effects. Our method not only reduces communication costs during the training process, but also maintains accuracy similar to the original model. We conducted experimental validation using classic models and datasets, and evaluated our scheme and traditional federated learning scheme in terms of model accuracy, communication cost, and computational cost.

关键词： federated learning communication-efficient model compression

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SLAP-CC: Set-Level Adaptive Prefetching for Compressed Caches 40

SLAP-CC: Set-Level Adaptive Prefetching for Compressed Cache...

引用

ieee 40th International conference on Computer Design (ICCD)

作者： AlBarakat, Laith M. Gratz, Paul, V Jimenez, Daniel A. Texas A&M Univ Dept Elect & Comp Engn College Stn TX 77843 USA Texas A&M Univ Dept Comp Sci & Engn College Stn TX USA

ISBN: (数字)9781665461863

ISBN: (纸本)9781665461863

data prefetching and cache compression are well-studied techniques to reduce the impact of memory latency. data prefetching predicts future memory accesses and prefills the cache with the corresponding memory blocks in advance of explicit demands. Cache compression tries to increase the effective capacity of the cache with minimum area overhead. As we show, however, naively integrating the two techniques does not yield additive gains. We show that the extra ways per set that cache compression provides generally produce fewer hits than the ways in the baseline uncompressed cache. Hence, prefetching more aggressively to those sets with compression-added ways and more conservatively to those sets with less compression provides substantial benefit. In this paper we present set-level adaptive prefetching for compressed caches (SLAP-CC), a compressibility aware prefetching technique that adapts prefetch aggressiveness to the workload compressibility to maximize prefetch coverage. SLAP-CC dynamically adjusts the prefetch confidence threshold on a per-set basis, based on the set's compression. SLAP-CC achieves a geometric mean speedup of 18.0% over a baseline system with compressed cache and no prefetching. Further, SLAP-CC significantly outperforms existing state of the art L2 prefetchers, SPP [1] and Best Offset [2] when combined with cache compression by an average of 7% and up to 25% on some workloads.

关键词： Additives Filtering Prefetching data compression compression algorithms

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Burrows-Wheeler Transform on Purely Morphic Words

Burrows-Wheeler Transform on Purely Morphic Words

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data compression conference (DCC)

作者： Frosini, A. Mancini, I. Rinaldi, S. Romana, G. Sciortino, M. Univ Firenze Florence Italy Univ Siena Siena Italy Univ Palermo Palermo Italy

ISBN: (纸本)9781665478939

The study of the compressibility of repetitive sequences is an issue that is attracting great interest. We consider purely morphic words, which are highly repetitive sequences generated by iterating a morphism $\\varphi$ that admits a fixed point (denoted by $\\varphi^{\\infty}(a)$) starting from a given character $a$ belonging to the finite alphabet $A$, i.e. $\\varphi^{\\infty}(a)=\\lim\\nolimits_{i\\rightarrow\\infty}\\varphi^{i}(a)$. Such morphisms are called prolongable on $a$. Here we focus on the compressibility via the Burrows-Wheeler Transform ($BWT$) of infinite families of finite sequences generated by morphisms. In particular, denoted by $r(w)$ the number of equal-letter runs of a word $w$, we provide new upper bounds on $r(\\mathsf{bwt} (\\varphi^{i}(a)))$, i.e. the number of equal-letter runs produced when $BWT$ is applied on $\\varphi^{i}(a)$. Such bounds depend on the factor complexity $f_{x}(n)$ of the infinite word $x=\\varphi^{\\infty}(a)$, that counts, for each $n\\geq 0$, the number of distinct factors of $x$ having length $n$.

关键词： Upper bound data compression Transforms Complexity theory

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Joint Sensing, compression and Communication for Satellite-Terrestrial Integrated Networks 23

Joint Sensing, Compression and Communication for Satellite-T...

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23rd ieee International conference on Communication Technology, ICCT 2023

作者： Huang, Ning Dou, Chenglong Wu, Yuan Qian, Liping Lin, Bin Su, Zhou University of Macau State Key Laboratory of Internet of Things for Smart City China Zhuhai Um Science and Technology Research Institute Zhuhai China College of Information Engineering Zhejiang University of Technology Hangzhou China Dalian Maritime University Department of Communication Engineering Dalian China School of Cyber Science and Engineering Xi'an Jiaotong University Xi'an China

ISBN: (纸本)9798350325959

Satellite-terrestrial integrated network (STIN) has been regarded as a key paradigm in future 6G networks for providing seamless sensing and communication capabilities. In particular, satellites can be leveraged to remotely sense the environment on the earth and deliver the sensing data to the terrestrial base station (BS) for analysis. In this work, we study the joint sensing, compression and communication strategies for STIN, with the objective of maximizing the collected sensing data that can be forwarded to the terrestrial BS. Specifically, we formulate a joint optimization of the sensing rate, compression ratio, CPU frequency and the transmission power of the satellite. We further propose a layered yet efficient algorithm to solve the formulated non-convex joint optimization problem. Numerical results are provided to validate the performance of our proposed joint sensing, compression and communication strategies for STIN. © 2023 ieee.

关键词： data compression joint sensing and communications Satellite-terrestrial integrated network

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Non-Linear Mapping for Image Enhancement

Non-Linear Mapping for Image Enhancement

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data compression conference (DCC)

作者： Liu, Shouxin He, Lei Li, Yanyan Long, Wei Sichuan Univ 24 South Sect 1Yihuan Rd Chengdu 610065 Peoples R China

ISBN: (纸本)9781665478939

The existing low-light image enhancement methods may cause under enhancement, unbalanced brightness and blurriness. To address these shortcomings, we proposed the non-linear mapping method based on the Retinex theory (NMMR). We use an improved traditional gamma function to estimate the reflectance, and we proposed the maximum brightness channel to estimate the illumination. The main steps of this approach can be described as follows: First, we convert the image from the RGB (red, green, blue) color space to the HSV (hue, saturation, value) color space and the V channel is processed to estimate the reflectance and illumination. Then, we use a piecewise function to stretch the gray level dynamic range to achieve contrast enhancement. Finally, we use the fast pixelwise method to correct the color saturation and convert the image to the RGB color space. The experimental results show that the proposed method has lower computational complexity, the enhanced image has better objective and subjective evaluation results than other state-of-the-art methods.

关键词： Reflectivity Image color analysis Brightness Lighting Channel estimation data compression Dynamic range

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Lost in compression: the Impact of Lossy Image compression on Variable Size Object Detection within Infrared Imagery

Lost in Compression: the Impact of Lossy Image Compression o...

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

作者： Bhowmik, Neelanjan Barker, Jack W. Gaus, Yona Falinie A. Breckon, Toby P. Univ Durham Dept Comp Sci Durham England Univ Durham Dept Engn Durham England

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Lossy image compression strategies allow for more efficient storage and transmission of data by encoding data to a reduced form. This is essential enable training with larger datasets on less storage-equipped environments. However, such compression can cause severe decline in performance of deep Convolution Neural Network (CNN) architectures even when mild compression is applied and the resulting compressed imagery is visually identical. In this work, we apply the lossy JPEG compression method with six discrete levels of increasing compression {95, 75, 50, 15, 10, 5} to infrared band (thermal) imagery. Our study quantitatively evaluates the affect that increasing levels of lossy compression has upon the performance of characteristically diverse object detection architectures (Cascade-RCNN, FSAF and Deformable DETR) with respect to varying sizes of objects present in the dataset. When training and evaluating on uncompressed data as a baseline, we achieve maximal mean Average Precision (mAP) of 0.823 with Cascade R-CNN across the FLIR dataset, outperforming prior work. The impact of the lossy compression is more extreme at higher compression levels (15, 10, 5) across all three CNN architectures. However, re-training models on lossy compressed imagery notably ameliorated performances for all three CNN models with an average increment of similar to 76% (at higher compression level 5). Additionally, we demonstrate the relative sensitivity of differing object areas {tiny, small, medium, large} with respect to the compression level. We show that tiny and small objects are more sensitive to compression than medium and large objects. Overall, Cascade R-CNN attains the maximal mAP across most of the object area categories.

关键词： Training Image coding Sensitivity Transform coding Object detection Computer architecture Transformers

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DEEP FEATURE compression USING RATE-DISTORTION OPTIMIZATION GUIDED AUTOENCODER 29

DEEP FEATURE COMPRESSION USING RATE-DISTORTION OPTIMIZATION ...

引用

ieee International conference on Image Processing (ICIP)

作者： Yamazaki, Meguru Kora, Yuichiro Nakao, Takanori Lei, Xuying Yokoo, Kaoru Fujitsu Ltd Tokyo Japan

ISBN: (数字)9781665496209

ISBN: (纸本)9781665496209

Collaborative intelligence (CI) has been proposed to efficiently utilize computational resources on edge devices over edge-cloud environments. To realize this, a deep neural network (DNN) model is divided into two parts, which are deployed to the edge device and cloud separately so that they can perform DNN inference collaboratively. Feature compression methods have been investigated to reduce the traffic volume between the edge and cloud because the data size of a feature map, which is the output of the edge device in CI, is relatively larger than that of the compressed original image. Although feature compression methods can achieve a good compression rate, the traffic volume is not small enough because the data size of a feature map might exceed that of the original image. Herein, we propose a novel feature compression method that utilizes an autoencoder (AE) to compress the feature map. In image compression, distortion and data size are minimized, whereas in our method, the AE is trained to minimize the prediction error and data size. In this way, the AE can extract only the data needed for the task of the DNN model and considerably reduce the data size while maintaining the prediction accuracy. In performance evaluation, we apply the proposed method to widely used convolutional neural network models: VGG16, ResNet50, and YOLOv3. Our results confirm that the proposed method drastically reduces the traffic volume while utilizing the resources of edge devices in CI. Our proposed method reduces the traffic volume by 62-99%, 16-99%, and 35-53% compared to versatile video coding in each model.

关键词： Feature compression Collaborative Intelligence Autoencoder Deep Learning

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Seque: Lean and Energy-aware data Management for IoT Gateways 7

Seque: Lean and Energy-aware Data Management for IoT Gateway...

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7th ieee International conference on Edge Computing and Communications (ieee EDGE) / ieee World Congress on Services (SERVICES)

作者： Sixdenier, Pierre-Louis Wildermann, Stefan Ottens, Martin Teich, Juergen Friedrich Alexander Univ Erlangen Nurnberg Erlangen Germany

ISBN: (纸本)9798350304831

IoT systems with multiple deployed sensor nodes often use gateways to gather, fuse, transform and transmit diverse data acquired from the sensor nodes, e.g., to a cloud server. When being deployed in remote environments, not only the memory and storage, but also energy can be scarce and supply be time-dependent and often unpredictable, e.g. when obtained by energy harvesting. In this realm, this paper proposes a lean and energy-aware methodology called Seque for data management for such gateways. Rather than processing multiple sensor requests at a time and being unconscious of the level of available energy, Seque schedules only one request at a time. Moreover, Seque dynamically decides whether to directly process and transmit data of a request to a cloud server, or alternatively compress and persist data locally on the gateway in expectation of a power failure to postpone the upload to time of recovery from a power shortage. With this scheduling technique, a guarantee can be given that no sensor request admitted will suffer from partial or full loss of data. A reference implementation of Seque is provided with scheduling decisions being calibrated based on energy models of sensor interfaces, CPU system and upload interfaces of a real embedded gateway platform. Presented analysis on whether energy can be sent most by selective compression of data. Finally, the lightweight approach is evaluated in terms of energy consumption, and storage footprint and compared with commercially available database management systems including MongoDB and SQLite. The evaluation shows that Seque provides on average between 51% and 63% lower energy consumption for different data schemas per sensor request and also between 63% and 78% of lower storage requirements, pronouncing its leanness.

关键词： IoT Power Management Gateway Energy Harvesting data Management

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Construction of Video Resources data Warehouse System Based on Hive

Construction of Video Resources Data Warehouse System Based ...

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2023 International conference on data Science and Network Security, ICDSNS 2023

作者： You, Aixue College of Foreign Languages Liaoning Institute of Science and Engineering Jinzhou China

ISBN: (纸本)9798350301595

With the continuous development of computer technology and the Internet, as well as the arrival of 5G era, the rapid growth of data, data warehouse has played a huge role in various practical applications. Massive English video teaching resources promote English teaching. How to manage these resources effectively becomes a difficult problem. A data warehouse system of English video resources is built based on Hive to convert various English video resources and eventually form a data set. Firstly, the Hive data warehouse tool is studied. Then, the compression coding of H.266/VVC video is studied, including intra-frame prediction, transform coding and quality evaluation. Finally, the video resource migration based on Sqoop tool is studied. The research results significantly enhance the compression performance of encoders, and have the advantages of low cost, high performance and high fault tolerance in modern data warehouse. © 2023 ieee.

关键词： Image compression

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