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

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Text data Anti Pinch Technology Based on Visual Font Filtering Binary Security Detection 2

Text Data Anti Pinch Technology Based on Visual Font Filteri...

引用

2nd ieee International conference on Control, Electronics and Computer Technology, ICCECT 2024

作者： Li, Kun Li, Yuqi Ge, Yong The First Research Institute of the Ministry of Public Security Customs Protection Technology Division Beijing China Customs Protection Support Center Beijing Zhongdun Anxin Technology Development Co. Ltd Beijing China

ISBN: (纸本)9798350380958

Text entrainment attacks target users by embedding malicious information in text data. Due to malicious information being hidden in normal text data and difficult to detect, this attack method has a high successful rate. This paper proposes a new anti pinch technique for binary text compression attacks. Firstly, one-dimensional ordinary text data is transformed into two-dimensional data based on the dot matrix or vector graphs. Second, the text character's library based on FreeType is designed, which utilizes the characteristics of FreeType's character library to filter glyph and vector data during font file parsing. Finally, specific binary format characters or vector characters are checked in these data to quickly and efficiently discover potential embedded binary data, effectively preventing malicious attacks by binary code during transmission, storage, and use, ensuring the security and integrity of text data. © 2024 ieee.

关键词： Digital storage

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Fractional Motion Estimation for Point Cloud compression

Fractional Motion Estimation for Point Cloud Compression

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data compression conference (DCC)

作者： Hong, Haoran Pavez, Eduardo Ortega, Antonio Watanabe, Ryosuke Nonaka, Keisuke Univ Southern Calif Los Angeles CA 90089 USA KDDI Res Inc Fujimino Saitama Japan

ISBN: (纸本)9781665478939

Motivated by the success of fractional pixel motion in video coding, we explore the design of motion estimation with fractional-voxel resolution for compression of color attributes of dynamic 3D point clouds. Our proposed block-based fractional-voxel motion estimation scheme takes into account the fundamental differences between point clouds and videos, i.e., the irregularity of the distribution of voxels within a frame and across frames. We show that motion compensation can benefit from the higher resolution reference and more accurate displacements provided by fractional precision. Our proposed scheme significantly outperforms comparable methods that only use integer motion. The proposed scheme can be combined with and add sizeable gains to state-of-the-art systems that use transforms such as Region Adaptive Graph Fourier Transform and Region Adaptive Haar Transform.

关键词： Point cloud compression Video coding Three-dimensional displays Adaptive systems Image color analysis Motion estimation Dynamics

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A CTU-Level Rate Control Algorithm for Screen Content Based on GLCM Characteristic Extraction 2

A CTU-Level Rate Control Algorithm for Screen Content Based ...

引用

2nd ieee International conference on Signal, Information and data Processing, ICSIDP 2024

作者： Liu, Zongpei He, Chenlong Jing, Minge Liu, Zherong Zhou, Jun Fan, Yibo Fudan University China University of Electronic Science and Technology of China China

ISBN: (纸本)9798331515669

With the rapid development of interactive services such as cloud gaming and cloud desktop, the efficient compression of screen content has become increasingly important. These services offload application storage and computation to the cloud, reducing reliance on local hardware and emphasizing the need for high-quality video transmission over networks. This paper addresses the challenges in screen content coding (SCC) by proposing a novel rate control algorithm tailored for screen content, which is distinct from traditional video due to its unique characteristics like sharp text edges and repetitive graphic patterns. Our approach leverages the Grey Level Co-occurrence Matrix (GLCM) feature extraction to perform texture complexity analysis at the Coding Tree Unit (CTU) level, optimizing bit allocation based on the texture's angular second moment (ASM). We introduce a method to estimate the similarity of adjacent blocks and dynamically adjust bit allocation to account for this. The proposed method ensures low latency and stable bitrate, crucial for cloud gaming and desktop applications. Extensive experiments on the HM-16.10+SCM-7.0 and HCM-16.20+SCM-8.8 demonstrate that our approach significantly improves bitrate accuracy and compression efficiency while achieving superior subjective video quality. The algorithm's performance is benchmarked against state-of-the-art methods, showcasing its effectiveness in various screen content scenarios. © 2024 ieee.

关键词： Image compression

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Context-Aware Neural Video compression on Solar Dynamics Observatory 22

Context-Aware Neural Video Compression on Solar Dynamics Obs...

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22nd ieee International conference on Machine Learning and Applications, ICMLA 2023

作者： Khoshkhahtinat, Atefeh Zafari, Ali Mehta, Piyush M. Nasrabadi, Nasser M. Thompson, Barbara J. Kirk, Michael S. F. Da Silva, Daniel West Virginia University Dept. of Computer Science & Electrical Engineering WV United States West Virginia University Dept. of Mechanical & Aerospace Engineering WV United States Nasa Goddard Space Flight Center MD United States

ISBN: (纸本)9798350345346

NASA's Solar Dynamics Observatory (SDO) mission collects large data volumes of the Sun's daily activity. data compression is crucial for space missions to reduce data storage and video bandwidth requirements by eliminating redundancies in the data. In this paper, we present a novel neural Transformer-based video compression approach specifically designed for the SDO images. Our primary objective is to efficiently exploit the temporal and spatial redundancies inherent in solar images to obtain a high compression ratio. Our proposed architecture benefits from a novel Transformer block called Fused Local-aware Window (FLa Win), which incorporates window-based self-attention modules and an efficient fused local-aware feed-forward (FLaFF) network. This architectural design allows us to simultaneously capture short-range and long-range information while facilitating the extraction of rich and diverse contextual representations. Moreover, this design choice results in reduced computational complexity. Experimental results demonstrate the significant contribution of the FLaWin Transformer block to the compression performance, outperforming conventional hand-engineered video codecs such as H.264 and H.265 in terms of rate-distortion trade-off. © 2023 ieee.

关键词： FLaWin Neural Video compression Solar Dynamics Observatory Swin Transformer

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Entropy Modeling in Video compression Based on Machine Learning 9

Entropy Modeling in Video Compression Based on Machine Learn...

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9th ieee International conference on Information Technology and Nanotechnology, ITNT 2023

作者： Yakubenko, M.A. Gashnikov, M.V. Samara National Research University Samara Russia

ISBN: (纸本)9798350397338

The paper investigates an entropy model of spatio-temporal characteristics in video compression using machine learning algorithms. A significant drawback of the vast majority of existing works on neural video codecs is the emphasis on the process of optimizing the creation of a latent representation through the study of various network structures, using well-studied models for image compression as an entropy model The model under study makes it possible to effectively evaluate both the spatial and temporal characteristics of compressed video data, which makes it possible to achieve a greater reduction in video redundancy. In addition, the universality of the entropy model also allows you to set the quantization step over the spatial channel. This content-adapted quantization mechanism, similar to rate control in standard codecs, not only helps achieve smooth compression ratio adjustment, but also improves final performance by dynamically distributing quantization intervals. The results of computational experiments on real video sequences confirm the efficiency of the studied video compression method. © 2023 ieee.

关键词： Image compression

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Image compression using Auto-Encoders in Deep learning

Image compression using Auto-Encoders in Deep learning

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2024 OPJU International Technology conference on Smart Computing for Innovation and Advancement in Industry 4.0, OTCON 2024

作者： Goel, Pawan Kumar Agarwal, Vertika Verma, Shalini Aeron, Anurag Jayant, Chanchal Singh, Shailendra Raj Kumar Goel Institute of Technology Department of Cse U.P Ghaziabad India Miet Meerut Department of Cse U.P Meerut India Inderprastha Engineering College Department of Cse U.P Ghaziabad India

ISBN: (纸本)9798350373783

Image compression is a crucial aspect of handling vast amounts of visual data in applications ranging from multimedia communication to storage efficiency. This research paper explores the application of deep learning, specifically auto-encoders, for image compression. We employ auto-encoders, a class of neural networks, to learn efficient representations of images by compressing them into a lower-dimensional space. The paper introduces a systematic methodology for leveraging auto-encoders in the image compression process, emphasizing pre-processing, model architecture, training, and evaluation. We conduct practical experiments to demonstrate the effectiveness of the proposed approach, showcasing significant reductions in file sizes while retaining essential visual information. The study also identifies limitations and suggests future directions, including the exploration of transformer architectures to address challenges associated with small-sized images. We discuss the evolving landscape of deep learning in image compression, highlighting the potential of these techniques to reshape data handling strategies for visual information. © 2024 ieee.

关键词： Image coding

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A Framework Using Absolute compression Hard-Threshold for Improving The Robustness of Federated Learning Model 26

A Framework Using Absolute Compression Hard-Threshold for Im...

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26th International conference on Computer Supported Cooperative Work in Design, CSCWD 2023

作者： Wu, Yuzhang Wang, Beilun Southeast University School of Computer Science and Engineering Nanjing China

ISBN: (纸本)9798350331684

Nowadays, with the popularity of the federated learning, it becomes crucial for us to tackle the challenges, communication cost and model robustness. And targeting at the communication bottleneck, data compression is widely used to solve the problem. Besides, the usage of variance reduction for achieving robustness and communication compression for reducing costs has been studied. The Byz-VR-MARINA pro- posed before uses random-sparsification. In this paper, we adopt the absolute compressors hard-threshold and propose a robust compressed framework Byz-VR-BARRY. Experimental results on w8a and a9a datasets have shown the effectiveness of our method, which can decrease the optimality gap obviously. © 2023 ieee.

关键词： data compression

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ST-GNN-Powered compression for Latency Reduction in Hybrid Satellite-Terrestrial Networks 8

ST-GNN-Powered Compression for Latency Reduction in Hybrid S...

引用

8th International conference on Information, Control, and Communication Technologies, ICCT 2024

作者： Do, Phuc Hao Due Le, Tran Berezkin, Aleksandr Kirichek, Ruslan The Bonch-Bruevich Saint - Petersburg State University of Telecommunications Faculty of Infocommunication Networks and Systems Saint-Petersburg Russia University of Wisconsin-Stout Department of Mathematics Statistics & Computer Science MenomonieWI United States

ISBN: (纸本)9798331517564

Hybrid Satellite-Terrestrial Communication Networks (HSTCNs) have emerged as a critical infrastructure for modern communication, integrating the capabilities of satellite and terrestrial networks to meet the increasing demands for connectivity and reliability. However, enhancing data transmission efficiency in such heterogeneous networks remains challenging, particularly concerning latency. This research aims to address this challenge by proposing a lossless compression method leveraging custom Spatio-Temporal Graph Neural Network (ST-GNN) architectures to reduce data transmission latency in HSTCNs. The primary objectives of this study are twofold: first, to evaluate the compression rates achieved by different ST-GNN-based network traffic compression methods, and second, to conduct a comparative analysis of data transmission latency over HSTCNs based on the compression of traffic networks. By exploiting the inherent spatio-temporal characteristics of communication data, ST- GNNs offer a promising approach to compress network traffic while minimizing latency effectively. The empirical assessment demonstrates the effectiveness of the proposed ST-GNN-based compression methods in achieving competitive compression rates compared to existing techniques. Furthermore, the comparative analysis highlights the potential of ST-GNNs in improving data transmission efficiency and reducing latency in HSTCNs. © 2024 ieee.

关键词： Satellite communication systems

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Survey On Image compression Algorithms And Their Efficiencies Under Different Types Of Files 6

Survey On Image Compression Algorithms And Their Efficiencie...

引用

6th International conference on Recent Trends in Advance Computing, ICRTAC 2023

作者： Nithya, P. Sathya, M. Vengattaraman, T. Pondicherry University Department of Computer Science India

ISBN: (纸本)9798350394702

Images produce massive amounts of data, which puts a burden on both storage and transmission. The compression makes sure all the original data can be compressed and after compression, decompressing data are restored. While utilizing lossy compression, a fraction of the initial image's data is discarded. It puts a lot of pressure on both transmission and storage. In order to reduce the quantity of data or storage needed to represent an image, various algorithms and techniques are employed, though this involves a compromise in quality. Image compression is frequently used to provide successful picture and display while maintaining the balance between image quality and file size. Image compression uses different algorithm to compress a different types of image file. For Our study we took three major algorithms of losslesscompression are run-length coding, Huffman coding, Lempel-Ziv-welch. And Result shows the original image size, compressed size, compression ratio, bits per pixel (BPP). This kind of study is helpful for understanding how well image compression and lossless algorithms working on various datasets with respect to image. © 2023 ieee.

关键词： Image compression

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Learned Video compression With Refined Adaptive Flow Pyramid And Coordinate-Aware Attention

Learned Video Compression With Refined Adaptive Flow Pyramid...

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

作者： Huang, Qian Liu, Wenting Li, Xin Wang, Yiming College of Computer Science and Software Engineering Hohai University Nanjing China Key Laboratory of Water Big Data Technology Ministry of Water Resources Hohai University Nanjing China

ISBN: (纸本)9798350368741

In video compression, motion estimation and motion compensation are critical for achieving efficient encoding. Although the commonly used SpyNet and bilinear interpolation have contributed in improving the compression efficiency, they still have limitations. SpyNet often loses details and fails to fully utilize the feature extraction capabilities of deep networks. Furthermore, bilinear interpolation inherently attenuates high-frequency information, leading to frame blurring and distortion. In this paper, we propose a novel video compression algorithm. To overcome the limitations of SpyNet, we propose a refined adaptive flow pyramid network. This network uses a multi-scale feature pyramid to capture more details. Moreover, we use an iterative cost volume refinement engine that improves the feature representation of the network and iteratively improve the accuracy of motion estimation. In addition, to overcome the limitation of bilinear interpolation, we propose a coordinate-aware attention module, which captures more high-frequency information to improve the accuracy of motion compensation. Experimental results show that our method outperforms VTM-13.2 (LDP) in terms of PSNR. © 2025 ieee.

关键词： coordinate network cross attention flow pyramid learned video compression

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