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

共 16713 条记录，以下是341-350 订阅

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A Model compression Method Using Significant data and Knowledge Distillation

A Model Compression Method Using Significant Data and Knowle...

引用

ieee International Performance, Computing, and Communications conference (IPCCC)

作者： Zhang, Fan Xu, Beibei Cui, Kai Zhejiang Univ Hangzhou Peoples R China Nanjing Tech Univ Nanjing Peoples R China

ISBN: (纸本)9798350302936

With the emergence of complex neural network models, the running cost of the models shows an increasing trend, while the larger the model, the exponential increase in training cost. Therefore, this paper proposes a method to compress the model of Convolutional Neural Network (CNN) by combining data selection and knowledge distillation, so that the performance of the original model can be maintained with low computational cost. (1) For data selection, we propose an algorithm to rank the importance of training data based on influence scores, and select some training data with greater improvement in model performance as the new training data set based on the ranking results. (2) A knowledge distillation method with low data volume is proposed to input some important training data into the teacher model to generate knowledge to guide the training of the student model, so as to reduce the model running cost while maintaining the model performance of the teacher model, and such a method can also availably avoid the overfitting phenomenon when the data volume is too small. By compressing both the data and the model, the training and running costs of the model can be effectively reduced.

关键词： Deep learning Model compression Knowledge distillation data selection Important knowledge

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Enhancing Arabic Audio Quality through Neural Network-Based Upscaling 7

Enhancing Arabic Audio Quality through Neural Network-Based ...

引用

7th ieee International conference on Big data and Artificial Intelligence, BDAI 2024

作者： Alsuhaibani, Mohammed Qassim University College of Computer Department of Computer Science Buraydah Saudi Arabia

ISBN: (纸本)9798350352009

The ubiquity of data and its capture methods play a pivotal role in information communication. With the advent of online platforms, organizations extensively use compression techniques to mitigate costs and reduce network traffic, often at the expense of audio quality. This paper addresses this challenge by utilising an approach to audio data upscaling through neural network. The method specifically targets the generation of missing high-frequency components, facilitating speech super-resolution and bandwidth expansion. A key distinction of this work is its focus on Arabic audio content, a domain that has seen limited exploration in the context of neural network-based audio enhancement. Along with the development and introduction of an Arabic audio dataset curated for the purposes of this study. Experimental results validate the efficacy of the proposed approach, demonstrating marked improvements in audio quality. © 2024 ieee.

关键词： Bandwidth compression

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Multi-source data Lossless compression via Parallel Expansion Mapping and xLSTM

Multi-source Data Lossless Compression via Parallel Expansio...

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2025 ieee International conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Ma, Huidong Sun, Hui Yi, Liping Liu, Xiaoguang Wang, Gang College of Computer Science NBJL ICIC TMCC SysNet DISSec GTIISC Nankai University Tianjin300350 China Nanyang Technological University Nanyang639798 Singapore

ISBN: (纸本)9798350368741

Explosive growth of multi-source data (MSD) poses challenges in data transmitting and storing. Neural Network (NN)-based lossless compressors are an important type of compression approaches to alleviate these problems. However, existing NN-based lossless compressors suffer from poor compression ratio and high time cost at the same time. To address these issues, we propose a novel MSD Lossless Compressor (MSDLC) with two compression stages: 1) We propose a Parallel Expansion Mapper (PEM) to map redundant pieces in MSD into unused alphabet values, which not only compresses MSD but also saves time for the next stage's NN-based lossless compression. 2) With the mapped MSD as input, we design a NN-based lossless compressor to further improve compression ratio, where we introduce the state-of-the-art xLSTM model and design a Deep Spatial Gating Module (DSGM) as the backbone of NN. We compare MSDLC with 11 baselines on 6 real-world datasets and the results validate that MSDLC obtains the best average compression ratio and time cost. Compared with baselines, compression ratios are improved by 1.103%∼113.897%, and the time costs are improved by 41.367%∼73.891%. The codes can be available at https://***/mhuidong/MSDLC. © 2025 ieee.

关键词： Deep Spatial Gating Module Lossless compression Multi-source data Parallel Expansion Mapping xLSTM

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COMMUNICATION-EFFICIENT FEDERATED LEARNING THROUGH ADAPTIVE WEIGHT CLUSTERING AND SERVER-SIDE DISTILLATION 49

COMMUNICATION-EFFICIENT FEDERATED LEARNING THROUGH ADAPTIVE ...

引用

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

作者： Tsouvalas, Vasileios Saeed, Aaqib Ozcelebi, Tanir Meratnia, Nirvana Eindhoven Univ Technol Eindhoven Netherlands

ISBN: (纸本)9798350344868;9798350344851

Federated Learning (FL) is a promising technique for the collaborative training of deep neural networks across multiple devices while preserving data privacy. Despite its potential benefits, FL is hindered by excessive communication costs due to repeated server-client communication during training. To address this challenge, model compression techniques, such as sparsification and weight clustering are applied, which often require modifying the underlying model aggregation schemes or involve cumbersome hyperparameter tuning, with the latter not only adjusts the model's compression rate but also limits model's potential for continuous improvement over growing data. In this paper, we propose FedCompress, a novel approach that combines dynamic weight clustering and server-side knowledge distillation to reduce communication costs while learning highly generalizable models. Through a comprehensive evaluation on diverse public datasets, we demonstrate the efficacy of our approach compared to baselines in terms of communication costs and inference speed. We will make our implementation public upon acceptance.

关键词： federated learning model compression weight clustering communication efficiency knowledge distillation

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Ant: An Efficient Lossless compression Algorithm for IoT Time Series data 21

Ant: An Efficient Lossless Compression Algorithm for IoT Tim...

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21st ieee International Symposium on Parallel and Distributed Processing with Applications, 13th ieee International conference on Big data and Cloud Computing, 16th ieee International conference on Social Computing and Networking and 13th International conference on Sustainable Computing and Communications, ISPA/BDCloud/SocialCom/SustainCom 2023

作者： Li, Junhui Xu, Guangping Yang, Hongzhang Wu, Yulei Tianjin University of Technology School of Computer Science and Engineering Tianjin China Tianjin Key Laboratory of Intelligence Computing and Novel Software Technology Tianjin China Key Laboratory of Computer Vision and System Ministry of Education China University of Bristol Faculty of Science and Engineering Bristol United Kingdom

ISBN: (纸本)9798350329223

In various applications in Internet of Things like industrial monitoring, large amounts of floating-point time series data are generated at an unprecedented rate. Efficient compression algorithms can effectively reduce the size of data, enhance transmission performance and storage efficiency, and simultaneously lower storage costs. Therefore, there is a need for lightweight and efficient stream compression algorithms. In this paper, we propose a novel lossless floating-point data compression algorithm called Ant. The main idea is to encode double-precision floating-point numbers into integer form, calculate the delta between adjacent values, and then convert the delta into unsigned integers. This encoding method effectively reduces storage costs and improves data compression efficiency. Extensive experiments on real-world datasets demonstrate that our algorithm achieves compression speeds at least as fast as state-of-the-art streaming methods, and a 63% relative improvement in average compression rate. © 2023 ieee.

关键词： Storage efficiency

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AdaStreamer: Machine-Centric High-Accuracy Multi-Video Analytics with Adaptive Neural Codecs

AdaStreamer: Machine-Centric High-Accuracy Multi-Video Analy...

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ieee conference on Computer Communications (ieee INFOCOM)

作者： Zhu, Andong Zhang, Sheng Cheng, Ke Shi, Xiaohang Qian, Zhuzhong Lu, Sanglu Nanjing Univ State Key Lab Novel Software Technol Nanjing Peoples R China

ISBN: (纸本)9798350383515;9798350383508

Increased videos captured by widely deployed cameras are being analyzed by computer vision-based Deep Neural Networks (DNNs) on servers rather than being streamed for humans. Unfortunately, the conventional codecs (e.g., H.26x and MPEG-x) originally designed for video streaming lack content-aware feature extraction and hinder machine-centric video analytics, making it difficult to achieve the required high accuracy with tolerable delay. Neural codecs (e.g., autoencoder) now hold impressive compression performance and have been widely advocated in video streaming. While autoencoder shows transformative potential, the application in video analytics is hampered by low accuracy in detecting small objects of high-resolution videos and the serious challenges posed by multivideo streaming. To this end, we propose AdaStreamer with adaptive neural codecs to enable real machine-centric high-accuracy multi-video analytics. We also investigate how to achieve optimal accuracy under delay constraints via careful scheduling in compression Ratios (CRs, the ratio of the compressed size to the original data size) and bandwidth allocation, and further propose a Markov-based Adaptive compression and Bandwidth Allocation algorithm (MACBA). We have practically developed a prototype of AdaStreamer, based on which extensive experiments verify its accuracy improvement (up to 15%) compared to state-of-the-art coding and streaming solutions.

关键词： Video Analytics Neural Codecs compression Optimization Bandwidth Allocation

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Video Transformer based Video Quality Assessment with Spatiotemporally adaptive Token Selection and Assembly

Video Transformer based Video Quality Assessment with Spatio...

引用

data compression conference (DCC)

作者： Zhao, Shiling Yin, Haibing Wang, Hongkui Zhou, Yang Hangzhou Dianzi Univ 1158 2 St Hangzhou 310018 Peoples R China Hangzhou Dianzi Univ Lishui Res Inst Lishui 323000 Peoples R China

ISBN: (纸本)9798350347951

Video quality assessment (VQA) for user generated content (UGC) videos plays important role in video compression and processing. Convolutional neural network (CNN) based quality assessment for UGC is the research focus with inspiring model accuracy increment in the past three years. However, regularly temporal-sampling with temporal feature loss, as well as fixed token selection strategy video transformer (ViT) with insufficient representational capacity of tokens, jointly degrade the accuracy of conventional ViT based quality assessment. Facing these two challenges, this article proposes an adaptive token-selection ViT (ATSViT) structure for UGC VQA. Accounting for the uneven distribution of spatiotemporal distortion-related features, this work proposes a timing block sampling (TBS) module to adaptively select video blocks and assemble them into content compacted subsequence for further processing. In addition, inspired by the mental filter theory in terms of visual information, we propose a stage-wise adaptive screening network (SSNet) in which "noise" features of tokens in the sense of perception are progressively detected and processed by imitating the behavior of perception process in the eye-brain system. Experimental results verify that the proposed VQA model achieves state-of-the-art (SOTA) accuracy, with the highest correlation with mean opinion scores (MOS).

关键词： Image compression

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LOCALIZATION OF IMAGE SPLICING UNDER SEGMENT ANYTHING MODEL WITH INTEGRATED compression AND EDGE ARTIFACTS 31

LOCALIZATION OF IMAGE SPLICING UNDER SEGMENT ANYTHING MODEL ...

引用

2024 International conference on Image Processing

作者： Zhao, Ruhao Zhong, Xian Liao, Liang Liu, Wenxuan Huang, Wenxin Wang, Zheng Wuhan Univ Technol Hubei Key Lab Transportat Internet Things Sch Comp Sci & Artificial Intelligence Wuhan Peoples R China Hubei Univ Sch Comp Sci & Informat Engn Wuhan Peoples R China Nanyang Technol Univ Rapid Rich Object Search Lab Sch Elect & Elect Engn Singapore Singapore Nanyang Technol Univ Coll Comp & Data Sci Singapore Singapore Wuhan Univ Natl Engn Res Ctr Multimedia Software Wuhan Peoples R China

ISBN: (纸本)9798350349405;9798350349399

The localization of image splicing involves identifying pixels in an image that have been spliced from other images, necessitating the discernment of splicing features. Despite significant advancements driven by the rise of social media and deep learning, existing methods exhibit limitations, often neglecting the integration of coarse and precise features and lacking the ability to understand objects. This leads to erroneous predictions in identifying spliced regions. This paper proposes Segment Anything Model with Integrated compression and Edge artifacts (SAM-ICE) for the localization of image splicing, addressing these limitations by fusing forged edge features and compression artifact features. Leveraging SAM's object understanding ability, our method identifies spliced regions using the fused features as guidance. Specifically, we employ Edge Artifact Extractor (EAE) to extract fine high-frequency edge splicing features and compression Artifact Extractor (CAE) to extract coarse compression artifact features. By combining these features, our method utilizes coarse-fine features to accurately pinpoint the spliced portions of the image. Experimental results demonstrate the superior accuracy, robustness, and generalizability of our method compared to the state-of-the-arts.

关键词： Image Splicing Forgery Localization Coarse-fine Feature Fusion Edge Splicing Features compression Artifacts

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MCEN: A Multi-modal compression and Encryption Network for Medical IoT

MCEN: A Multi-modal Compression and Encryption Network for M...

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ieee International conference on Communications (ieee ICC)

作者： He, Peng Meng, Shaoming Cui, Yaping Wu, Dapeng Wang, Ruyan Chongqing Univ Posts & Telecommun Sch Commun & Informat Engn Chongqing Peoples R China Chongqing Educ Commiss China Adv Network & Intelligent Connect Technol Key Lab Chongqing Peoples R China Chongqing Key Lab Ubiquitous Sensing & Networking Chongqing Peoples R China

ISBN: (纸本)9781538674628

As the Medical Internet of Things (MIoT) is rapidly evolving, increasing wearable devices are applied to collect various physiological signals for the purpose of medical applications. Currently, it's challenging to process multi-modal physiological signals on wearable devices because of their limitations of energy and computing resources, as well as the threat of data privacy leakage. To address ahead-mentioned problems, this paper designs a novel scheme to process multi-modal physiological signals, namely Multi-modal compression and Encryption Network (MCEN) with GAN and AE. The proposed MCEN accomplishes the encryption and compression of data by the adversarial property of GAN and the feature extraction property of AE. The proposed scheme aims to protect user privacy and reduce the consumption of energy and computing resources. In addition, the proposed MCEN is simulated using the Pulse Transit Time PPG dataset. The results show that the proposed scheme can well encrypt the data, and the compression performance is improved by 36.3% and 61.3% compared to the CAE and WT Algorithm respectively.

关键词： neural network compression encryption medical internet of things

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An Improvement to Merge Mode in ECM With Template Matching

An Improvement to Merge Mode in ECM With Template Matching

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data compression conference (DCC)

作者： Liao, Ru-Ling Ye, Yan Chen, Jie Li, Xinwei Alibaba Grp Hangzhou Peoples R China

ISBN: (纸本)9798350347951

In the development of video coding standard, decoder-side motion derivation technology has been proven to provide promising coding efficiency. With this type of technology, the motion information is derived at the decoder instead of being signaled in the bitstream by the encoder, and thus, the number of bits to be sent are reduced. A typical decoder-side derivation technology is template matching, which refines the motion by finding the closest match between neighboring reconstructed samples and corresponding reference samples in the reference pictures. In this paper, the template matching method is extended to temporal motion vector predictor, bi-prediction with CU-level weight and geometric partition modes in order to fully utilize its benefit. Specifically, template matching is used to determine the prediction direction and reference picture of temporal motion vector predictors, and to decide the bi-predicted weight of bi-prediction merge blocks. In addition, the motion of two geometric partitions are individually refined by the template matching mechanism. Simulation results show that on top of Enhanced compression Model (ECM), which is the software platform for exploring activities beyond versatile video coding established by the joint video experts team (JVET), the three proposed methods achieve 0.20% luma BD-rate savings in random access configuration and 0.33% luma BD-rate saving in low delay B configuration with negligible encoding and decoding runtime impact. It is worth noting that all three proposed methods have been adopted to the ECM software platform.

关键词： bi prediction with CU level weight Enhanced compression model geometric partition mode Template matching temporal motion vector predictor

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