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

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Practical Learned Lossless JPEG Recompression with Multi-Level Cross-Channel Entropy Model in the DCT Domain

Practical Learned Lossless JPEG Recompression with Multi-Lev...

引用

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

作者： Guo, Lina Shi, Xinjie He, Dailan Wang, Yuanyuan Ma, Rui Qin, Hongwei Wang, Yan Sense Time Res Hong Kong Peoples R China Natl Univ Def Technol Changsha Peoples R China Tsinghua Univ Beijing Peoples R China

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

JPEG is a popular image compression method widely used by individuals, data center;cloud storage and network filesystems. However, most recent progress on image compression mainly focuses on uncompressed images while ignoring trillions of already-existing JPEG images. To compress these JPEG images adequately and restore them back to JPEG format losslessly when needed, we propose a deep learning based JPEG recompression method that operates on DCT domain and propose a Multi-Level Cross-Channel Entropy Model to compress the most informative Y component. Experiments show that our method achieves state-of-the-art performance compared with traditional JPEG recompression methods including Lepton, JPEG XL and CMIX. To the best of our knowledge, this is the first learned compression method that losslessly transcodes JPEG images to more storage-saving bitstreams.

关键词： Deep learning data centers Computer vision Cloud computing Image coding Transform coding Entropy

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An Analysis of Transform Coding Techniques in Image and Video compression 15

An Analysis of Transform Coding Techniques in Image and Vide...

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15th International conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Goyal, Shobhit Kuppuraj, T. Bahri, Dimple Arora, Mithhil Dongre, Y.V. Chitra, S. Quantum University Research Center Quantum University India Department of Computer Applications Karpagam Academy of Higher Education Coimbatore India Karnataka Bangalore India Centre of Research Impact and Outcome Chitkara University Punjab Rajpura140417 India Vishwakarma Institute of Information Technology Pune India Department of Electronics and Communication Engineering Prince Shri Venkateshwara Padmavathy Engineering College Chennai India

ISBN: (纸本)9798350370249

this paper c the numerous transform coding strategies used for image and video compression when it comes to their effectiveness, implementation problems, and ability packages. Transform coding is an information compression approach that reduces the number of statistics by exploiting redundancies such as spatial and spectral correlations within the data. The primary remodel coding strategies mentioned encompass the discrete cosine rework (DCT), discrete wavelet remodel (DWT), block-based movement compensation (BMC), and changed discrete cosine remodel (MDCT). Furthermore, the paper compares the extraordinary remodel coding strategies in phrases in their performance, performance, and complexity. The paper starts by introducing the motivation at the back of rework coding techniques and their position in records compression. It then discusses how the numerous rework coding techniques compare to each other and why one technique can be favored over the alternative. The paper examines the overall performance of the special strategies by thinking about parameters including top sign-to-noise ratio (PSNR), compression fee, and objective/subjective nice. Finally, the paper affords an overview of the unique applications in which transform coding may be used. Standard, this paper affords an insightful evaluation of the diverse transform coding strategies used for information compression. ©2024 ieee.

关键词： Image compression

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Easy-To-Use First Information Report (FIR) System Using Blockchain 2

Easy-To-Use First Information Report (FIR) System Using Bloc...

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2nd International conference on Intelligent data Communication Technologies and Internet of Things, IDCIoT 2024

作者： Mistry, Tanya Sachin Gor, Bhavya Bhavesh Shukla, Rajkaran Krishna Sharma, Richa Dwarkadas J. Sanghvi College of Engineering Department of Information Technology Mumbai India

ISBN: (纸本)9798350327533

A First Information Report (FIR) is a legal document that serves as the foundation of criminal investigations and is filled by the police in certain countries, including India when they receive information about the commission of a cognizable offense. However, traditional methods of storing FIRs are often susceptible to tampering, loss, or theft. The suggested method employs a decentralized blockchain network to securely and openly store FIRs. Every FIR is stored in the blockchain as a transaction run by a network of nodes. Only authorized personnel, such as police officers can access the blockchain and view the FIRs. By using a blockchain-based system, FIRs can be made more secure, transparent, and efficient. The use of smart contracts can automate certain tasks related to FIRs, such as sending notifications to the complainant when the case is registered or updating the status of the case as it progresses through the legal system. Moving an FIR from one state to another can be a complex and time-consuming process, varying based on the state and offense type. To ensure the privacy and security of e-FIR data, encryption and compression techniques are employed. Smart contracts automate various FIR-related tasks, such as case progress notifications and status updates, improving efficiency and reducing human error. The paper explores the benefits of implementing e-FIR systems supported by blockchain and smart contracts, highlighting their potential to revolutionize the FIR management process and create a more accountable system. © 2024 ieee.

关键词： Blockchain compression encryption First Information Report (FIR) secure smart contract

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BPG-based compression of Poisson noisy images 13

BPG-based compression of Poisson noisy images

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13th International conference on Dependable Systems, Services and Technologies, DESSERT 2023

作者： Kovalenko, Bogdan Lukin, Vladimir National Aerospace University - KhAI Dept of Information-Communication Technologies Kharkiv Ukraine

ISBN: (纸本)9798350396119

The article is devoted to the lossy compression of noisy images, considering the currently popular trend of green computing. In this paper, we show how this trend can be incorporated into solving an essential task of image data transferring via communication lines and what benefits it can bring to this area of interest. We consider the case of images corrupted with signal-dependent Poisson-distributed noise, which is quite common in real-life situations. compression of noisy images has several peculiarities, among which there is the existence of optimal operation point (OOP), which is a value of compression control parameter (CCP) where a compressed image is closer to a noise-free image than the original noisy one. We show the possible presence of OOP for various visual quality metrics, both SSIM-based and PSNR-based. The recommendations for compression in OOP and its neighborhood are presented. © 2023 ieee.

关键词： Image compression

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Anomaly Detection in 3D Point Clouds using Deep Geometric Descriptors 23

Anomaly Detection in 3D Point Clouds using Deep Geometric De...

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23rd ieee/CVF Winter conference on Applications of Computer Vision (WACV)

作者： Bergmann, Paul Sattlegger, David Tech Univ Munich MVTec Software GmbH Munich Germany MVTec Software GmbH Munich Germany

ISBN: (纸本)9781665493468

We present a new method for the unsupervised detection of geometric anomalies in high-resolution 3D point clouds. In particular, we propose an adaptation of the established student-teacher anomaly detection framework to three dimensions. A student network is trained to match the output of a pretrained teacher network on anomaly-free point clouds. When applied to test data, regression errors between the teacher and the student allow reliable localization of anomalous structures. To construct an expressive teacher network that extracts dense local geometric descriptors, we introduce a novel self-supervised pretraining strategy. The teacher is trained by reconstructing local receptive fields and does not require annotations. Extensive experiments on the comprehensive MVTec 3D Anomaly Detection dataset highlight the effectiveness of our approach, which outperforms the existing methods by a large margin. Ablation studies show that our approach meets the requirements of practical applications regarding performance, runtime, and memory consumption.

关键词： Point cloud compression Training Location awareness Three-dimensional displays Runtime Protocols Memory management

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Neural Distributed Image compression Using Common Information

Neural Distributed Image Compression Using Common Informatio...

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data compression conference (DCC)

作者： Mital, Nitish Ozyilkan, Ezgi Garjani, Ali Gunduz, Deniz Imperial Coll London Dept Elect & Elect Engn London England NYU Dept Elect & Comp Engn New York NY 10003 USA Ecole Polytech Fed Lausanne Sect Math Lausanne Switzerland

ISBN: (纸本)9781665478939

We present a novel deep neural network (DNN) architecture for compressing an image when a correlated image is available as side information only at the decoder, a special case of the well-known distributed source coding (DSC) problem in information theory. In particular, we consider a pair of stereo images, which generally have high correlation with each other due to overlapping fields of view, and assume that one image of the pair is to be compressed and transmitted, while the other image is available only at the decoder. In the proposed architecture, the encoder maps the input image to a latent space, quantizes the latent representation, and compresses it using entropy coding. The decoder is trained to extract the common information between the input image and the correlated image, using only the latter. The received latent representation and the locally generated common information are passed through a decoder network to obtain an enhanced reconstruction of the input image. The common information provides a succinct representation of the relevant information at the receiver. We train and demonstrate the effectiveness of the proposed approach on the KITTI and Cityscape datasets of stereo image pairs. Our results show that the proposed architecture is capable of exploiting the decoder-only side information, and outperforms previous work on stereo image compression with decoder side information.

关键词： Image coding Image color analysis Source coding Neural networks Receivers Entropy coding Decoding

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An Internet of Things data compression Method Based on a Railway Project 2

An Internet of Things Data Compression Method Based on a Rai...

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2nd ieee International conference on data Science and Computer Application, ICDSCA 2022

作者： Chen, Haotian Yang, Wen Li, Xiya Xu, Jinglin Sun, Pengyu Yu, Cannan Corporation Limited Powerchina Huadong Engineering Zhejiang Hangzhou310014 China Powerchina Beijing100048 China

ISBN: (数字)9781665472005

ISBN: (纸本)9781665472005

Due to the rapid development of the Internet of Things, monitoring sensors have become increasingly popular in railway construction. The project has relatively high requirements for real-time monitoring, data storage, and rapid display to ensure construction safety. Therefore, data compression is a fundamental issue regarding time series generated by a wide range of sensors. For sequential data, a reasonable compression method should possess the following characteristics: a high degree of data reduction, the preservation of local and global features, and rapid response to user requests. The computer performs better as a result of several features. This paper utilizes a piecewise approximation algorithm to encode sequential data without compression. Comparatively to coding compression, this method can process and display data more quickly, and the compression rate is as high as 24%. © 2022 ieee.

关键词： data compression

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RECOGNITION-AWARE DEEP VIDEO compression FOR REMOTE SURVEILLANCE 29

RECOGNITION-AWARE DEEP VIDEO COMPRESSION FOR REMOTE SURVEILL...

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ieee International conference on Image Processing (ICIP)

作者： Beye, Florian Itsumi, Hayato Vitthal, Charvi Nihei, Koichi NEC Corp Ltd 1753 Shimonumabe Kawasaki Japan

ISBN: (数字)9781665496209

ISBN: (纸本)9781665496209

Image recognition techniques such as object detection are useful for assisting humans in remote video surveillance tasks. However, compression algorithms used for efficient video transmission are usually tuned for low reconstruction error and not for machine vision, leading to suboptimal recognition accuracies. In this work, we propose convolutional encoder-decoder neural networks for compressing video data intended for object detection. These networks are trained for optimal detection accuracy and bitrate, and make use of a novel stochastic quantization technique. In our experiments, we evaluate our method using publicly available datasets and show that we can substantially reduce bitrate over traditional codecs such as H.265 and also over other deep learning based compression methods at identical object detection accuracy. Moreover, we argue that perceived image quality of our compression method is close to H.265 at similar bitrates.

关键词： Video compression Object Detection

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Preventing Catastrophic Forgetting and Distribution Mismatch in Knowledge Distillation via Synthetic data 22

Preventing Catastrophic Forgetting and Distribution Mismatch...

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22nd ieee/CVF Winter conference on Applications of Computer Vision (WACV)

作者： Binici, Kuluhan Nam Trung Pham Mitra, Tulika Leman, Karianto ASTAR Inst Infocomm Res Singapore Singapore Natl Univ Singapore Sch Comp Singapore Singapore

ISBN: (纸本)9781665409155

With the increasing popularity of deep learning on edge devices, compressing large neural networks to meet the hardware requirements of resource-constrained devices became a significant research direction. Numerous compression methodologies are currently being used to reduce the memory sizes and energy consumption of neural networks. Knowledge distillation (KD) is among such methodologies and it functions by using data samples to transfer the knowledge captured by a large model (teacher) to a smaller one (student). However;due to various reasons, the original training data might not be accessible at the compression stage. Therefore, data-free model compression is an ongoing research problem that has been addressed by various works. In this paper, we point out that catastrophic forgetting is a problem that can potentially be observed in existing data-free distillation methods. Moreover;the sample generation strategies in some of these methods could result in a mismatch between the synthetic and real data distributions. To prevent such problems, we propose a data-free KD framework that maintains a dynamic collection of generated samples over time. Additionally, we add the constraint of matching the real data distribution in sample generation strategies that target maximum information gain. Our experiments demonstrate that we can improve the accuracy of the student models obtained via KD when compared with state-of-the-art approaches on the SVHN, Fashion MNIST and CIFAR100 datasets.

关键词： Deep learning Energy consumption Computer vision Computational modeling Neural networks Memory management Training data

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Comparative Study of compression Functions in Modern Web Programming Languages

Comparative Study of Compression Functions in Modern Web Pro...

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2023 International conference on Electromechanical and Energy Systems, SIELMEN 2023

作者： Tola, Krisel L. Technical University-Sofia Faculty of Telecommunications Sofia Bulgaria

ISBN: (纸本)9798350315240

With the increasing volume of data being generated and transferred every day, storage space has become a valuable resource. The huge size of these files, such as multimedia or software ones, have also a negative impact when it comes to the bandwidth usage they require while sharing them. On the other hand, it becomes even harder for this data to coexist in different operating systems and to offer the needed security since we face different types and formats of them. The file compression process comes as a solution to all of the above problems. It helps to optimize storage space, improve transfer speeds, enhance security, reduce bandwidth usage, and ensure compatibility across different platforms. Recently, the explosion of data generated by the Internet of Things (IoT), social media, and other sources has put even greater pressure on file compression technologies since they needed to apply to programming languages that run on web servers. As more and more data are generated, the need for efficient, effective compression algorithms becomes increasingly important. Studying the way these algorithms behave in a specific web programming language is also important. Based on historical usage and current trends in the web development industry regarding programming languages, Python, JavaScript, and PHP continue to be widely used and popular among developers. Some key factors contributing to their popularity include their ease of learning, extensive community support, versatility, web-focused features, integration capabilities, availability of frameworks, and improvements in performance and scalability. In this article, we will explore different optimization methods used for image and file compression and how they are integrated into modern web programming languages. © 2023 ieee.

关键词： data compression

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