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

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SILGAN: Generative Adversarial Networks for Multimedia data compression in Solar Insecticidal Lamps Internet of Things 49

SILGAN: Generative Adversarial Networks for Multimedia Data ...

引用

49th Annual conference of the ieee Industrial Electronics Society, IECON 2023

作者： Chen, Mingying Liu, Ye Shu, Lei Li, Kailiang Yang, Xing Yang, Fan Anhui Higher Education Institutes Key Laboratory of Architectural Acoustic Environment Hefei230601 China College of Artificial Intelligence Nanjing Agricultural University Nangjing China School of Engineering University of Lincoln United Kingdom School of Mathematics and Statistics Jiangsu Normal University Xuzhou221116 China

ISBN: (纸本)9798350331820

This paper presents a low overhead multimedia data compression method for Solar Insecticidal Lamps Internet of Things (SIL-IoTs), achieving efficient audio data transmission. First, the audio and video data generated by working solar insecticidal lamps are collected to form an original dataset in the solar insecticidal lamps applications. Then, we propose SILGAN, a generative adversarial network to compress multimedia audio data in the SIL-IoTs. Specifically, a depth-wise separable convolution is adopted to reduce the computational resources for training networks and running programs. Moreover, the network parameters are optimized so that obtaining a better neural network model with stable operation on the nodes of the SIL-IoTs. Finally, experimental evaluation is conducted, showing the effectiveness of the proposed SILGAN approach. © 2023 ieee.

关键词： data compression

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Adaptive Human-Centric Video compression for Humans and Machines

Adaptive Human-Centric Video Compression for Humans and Mach...

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2023 ieee/CVF conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2023

作者： Jiang, Wei Choi, Hyomin Racapé, Fabien InterDigital Inc. Emerging Technologies Lab United States

ISBN: (纸本)9798350302493

We propose a novel framework to compress human-centric videos for both human viewing and machine analytics. Our system uses three coding branches to combine the power of generic face-prior learning with data-dependent detail recovery. The generic branch embeds faces into a discrete code space described by a learned high-quality (HQ) codebook, to reconstruct an HQ baseline face. The domain-adaptive branch adjusts reconstruction to fit the current data domain by adding domain-specific information through a supplementary codebook. The task-adaptive branch derives assistive details from a low-quality (LQ) input to help machine analytics on the restored face. Adaptive weights are introduced to balance the use of domain-adaptive and task-adaptive features in reconstruction, driving trade-offs among criteria including perceptual quality, fidelity, bitrate, and task accuracy. Moreover, the proposed online learning mechanism automatically adjusts the adaptive weights according to the actual compression needs. By sharing the main generic branch, our framework can extend to multiple data domains and multiple tasks more flexibly compared to conventional coding schemes. Our experiments demonstrate that at very low bitrates we can restore faces with high perceptual quality for human viewing while maintaining high recognition accuracy for machine use. © 2023 ieee.

关键词： Image compression

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Deep Learning-Enabled Squirrel Detecting Prototype Development and Processing Analysis 20

Deep Learning-Enabled Squirrel Detecting Prototype Developme...

引用

20th ieee India Council International conference, INDICON 2023

作者： Sardana, Tushar Bitragunta, Sainath Birla Institute of Technology and Science Pilani Campus Department of Electrical and Electronics Engineering Rajasthan Pilani333031 India

ISBN: (纸本)9798350305593

In this paper, the authors present the development of an object detection prototype aimed at detecting living objects in front of vehicles. The proposed and implemented prototype focuses on detecting squirrels and alerting the driver to avoid collision. This can be further extended to other small animals. The prototype uses transfer learning to leverage pre-existing knowledge from a larger, more robust model and apply it to a smaller, more specific task. The model is able to reach an accuracy of greater than 95% on color images with the help of this approach. The detection models are designed to be implemented on small, low-power devices integrated into vehicle electronics that are useful for object detection. The paper further focuses on a detailed comparison of different compression techniques to reduce the size of the data required for classification, enabling the system to run more efficiently and effectively. Further, the drop in accuracy and the total inference time are compared for different compression techniques. The study uniquely compares the different image compression and inference processing techniques which can be used as a base for future studies. © 2023 ieee.

关键词： embedded systems image compression Object detection prototyping squirrel detection transfer learning

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Segmentation-based Initialization for Steered Mixture of Experts

Segmentation-based Initialization for Steered Mixture of Exp...

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2023 ieee International conference on Visual Communications and Image Processing, VCIP 2023

作者： Li, Yi-Hsin Sjostrom, Marten Knorr, Sebastian Sikora, Thomas Technical University of Berlin Germany Mid Sweden University Sweden Ernst-Abbe University of Applied Sciences Jena Germany

ISBN: (纸本)9798350359855

The Steered-Mixture-of-Experts (SMoE) model is an edge-Aware kernel representation that has successfully been explored for the compression of images, video, and higher-dimensional data such as light fields. The present work aims to leverage the potential for enhanced compression gains through efficient kernel reduction. We propose a fast segmentation-based strategy to identify a sufficient number of kernels for representing an image and giving initial kernel parametrization. The strategy implies both reduced memory footprint and reduced computational complexity for the subsequent parameter optimization, resulting in an overall faster processing time. Fewer kernels, when combined with the inherent sparsity of the SMoEs, further enhance the overall compression performance. Empirical evaluations demonstrate a gain of 0.3-1.0 dB in PSNR for a constant number of kernels, and the use of 23 % less kernels and 25 % less time for constant PSNR. The results highlight the feasibility and practicality of the approach, positioning it as a valuable solution for various image-related applications, including image compression. © 2023 ieee.

关键词： Image compression

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Signal compression with waves

Signal compression with waves

引用

2024 United States National Committee of URSI National Radio Science Meeting, USNC-URSI NRSM 2024

作者： Sounas, Dimitrios L. Wayne State University DetroitMI48202 United States

ISBN: (纸本)9781946815194

Signal compression is one of the most important operations in systems that are used to store or transmit information, since it allows efficient use of the available bandwidth or storage capacity. A typical compression system, such as those that are used in the JPEG standard, involves a linear matrix operation, like the discrete wavelet transform, followed by a series of nonlinear operations, such as quantization of the signal and application of a code. As in many other instances of signal processing systems, the matrix operation is the most computationally demanding one since its complexity scales nonlinearly with the size of the input data. Therefore, compression systems would benefit significantly if the involved matrix operation could be implemented in the analog domain and in a parallel manner without the use of active components. © 2024 ieee. All rights reserved.

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Point Cloud compression for Efficient data Broadcasting: A Performance Comparison

Point Cloud Compression for Efficient Data Broadcasting: A P...

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ieee Wireless Communications and Networking conference (ieee WCNC)

作者： Nardo, Francesco Peressoni, Davide Testolina, Paolo Giordani, Marco Zanella, Andrea Univ Padua Dept Informat Engn Padua Italy

ISBN: (纸本)9781665442664

The worldwide commercialization of fifth generation (5G) wireless networks and the exciting possibilities offered by connected and autonomous vehicles (CAVs) are pushing toward the deployment of heterogeneous sensors for tracking dynamic objects in the automotive environment. Among them, Light Detection and Ranging (LiDAR) sensors are witnessing a surge in popularity as their application to vehicular networks seem particularly promising. LiDARs can indeed produce a three-dimensional (3D) mapping of the surrounding environment, which can be used for object detection, recognition, and topography. These data are encoded as a point cloud which, when transmitted, may pose significant challenges to the communication systems as it can easily congest the wireless channel. Along these lines, this paper investigates how to compress point clouds in a fast and efficient way. Both 2D- and a 3D-oriented approaches are considered, and the performance of the corresponding techniques is analyzed in terms of (de)compression time, efficiency, and quality of the decompressed frame compared to the original. We demonstrate that, thanks to the matrix form in which LiDAR frames are saved, compression methods that are typically applied for 2D images give equivalent results, if not better, than those specifically designed for 3D point clouds.

关键词： LiDAR point cloud compression autonomous driving data broadcasting performance comparison

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VQ-VAE Empowered Wireless Communication for Joint Source-Channel Coding and Beyond

VQ-VAE Empowered Wireless Communication for Joint Source-Cha...

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

作者： Nemati, Mahyar Park, Jihong Choi, Jinho Deakin Univ Sch Informat Technol Geelong Vic Australia

ISBN: (纸本)9798350310900

Vector Quantized Variational Autoencoder (VQ-VAE) has shown promise in representing diverse and complex data distributions in deep learning, making it a potential solution for various applications including wireless communications. In this paper, we propose a joint source-channel coding scheme based on VQ-VAE for point-to-point wireless communication. Our approach leverages the dependence of the encoder and decoder on a given dataset and channel conditions to develop efficient encoding and decoding schemes, leading to improved reliability and efficiency even in the presence of noisy wireless channels. We demonstrate the effectiveness of our proposed approach through extensive simulations in handling realistic wireless communication scenarios. In addition, we discuss potential connections to semantic communication and highlight the secure and energy-efficient nature of our approach.

关键词： Joint Source and Channel Coding Lossy compression Noisy Channels VQ-VAE

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Simultaneous Denoising and compression for DVS with Partitioned Cache-Like Spatiotemporal Filter

Simultaneous Denoising and Compression for DVS with Partitio...

引用

2025 Design, Automation and Test in Europe conference, DATE 2025

作者： Zhao, Qinghang Ji, Yixi Wang, Jiaqi Wu, Jinjian Shi, Guangming School of Artificial Intelligence Xidian University Xi'an China

ISBN: (纸本)9783982674100

Dynamic vision sensor (DVS) is a novel neuromorphic imaging device that asynchronously generates event data corresponding to changes in light intensity at each pixel. However, the differential imaging paradigm of DVS renders it highly sensitive to background noise. Additionally, the substantial volume of event data produced in a very short time presents significant challenges for data transmission and processing. In this work, we present a novel spatiotemporal filter design, named PCLF, to achieve simultaneous denoising and compression for the first time. The PCLF employs a hierarchical memory structure that utilizes symmetric multi-bank cache-like row and column memories to store event data from a partitioned pixel array, which exhibits low memory complexity of O(m + n) for an m× n DVS. Furthermore, we propose a probability-based criterion to effectively control the compression ratio. We have implemented our design on an FPGA, demonstrating capabilities for real-time operation (≤ 60ns) and low power consumption ( © 2025 EDAA.

关键词： Cache memory

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Application of Various Novel Computer Image Processing (CIP) Algorithms in Digital Media Art Images

Application of Various Novel Computer Image Processing (CIP)...

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2025 International conference on Multi-Agent Systems for Collaborative Intelligence, ICMSCI 2025

作者： Du, Xian Shanghai Institute of Visual Arts Shanghai201620 China

ISBN: (纸本)9798331509828

With the continuous development of digital image processing algorithms, its application scenarios have been integrated from the simple research of a single image and a single algorithm to a multi-algorithm fusion analysis paradigm. Therefore, this study proposes a digital media art image analysis framework that combines multiple computer image processing (CIP) algorithms. This breakthrough covers three core architectures: image edge extraction, image style transfer, and image compression. In the image edge extraction module, this study designed an improved edge detection algorithm based on de-noising auto-encoder. This algorithm improves the accuracy of edge detection through multi-directional feature extraction and (I, O)-fuzzy rough set optimization, while maintaining global stability. In the image style transfer module, this study proposed a dual-module network. The network includes a texture translation network and a perceptual loss network, and achieves cross-domain style transfer through a fusion model. At the same time, the algorithm optimizes the perceptual loss function to enhance semantic matching capabilities. In the image compression module, this study uses chaotic systems to construct an optimized measurement matrix. Specifically, through a distributed data parallel training framework, a compression method based on the LPAC gradient sparse algorithm is constructed. By testing the three modules separately, the experimental results confirm the effectiveness of the proposed algorithm. © 2025 ieee.

关键词： Image compression

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Effective Pruning and compression of CNNs with Exponential Sparsity 5

Effective Pruning and Compression of CNNs with Exponential S...

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

作者： Mwelwa, Eunice Kabulo Zhang, Jun College of Automation Central South University Changsha China

ISBN: (纸本)9798331533694

Deep learning models are remarkably accurate and preformat, although they are frequently computationally expensive. Large quantities of memory and high-performance GPUs or TPUs are among the computing resources needed for training and deploying large-scale neural networks. Model optimization becomes indispensable. The process of enhancing deep learning models' effectiveness and efficiency is known as 'model optimization.' In this work, the proposed method for optimization is pruning. Pruning has proven to be effective in increasing inference efficiency, decreasing model size, and speeding up training without compromising accuracy. This paper introduces a pruning method leveraging exponential sparsity within each convolutional layer that varies from min_sparsity to max_sparsity and subsequent compression through sparse representations. Secondly, in this approach instead of the entire kernel sliding across the input data, it performs the one-by-one convolution, selecting slices with the important weights in each filter's kernel according to the sparsity target of that convolutional layer and setting all the other slices to zeros. The obtained results from the one-by-one convolution will be summed together to obtain the output map. Finally, the sparse model is converted into a TFLite model which is a converted format that can be used efficiently on edge devices using TensorFlow Lite. A framework that has been developed and evaluated with TensorFlow, leading to a smaller model size, and quicker inference times, and minimal accuracy loss compared to the original models. This study emphasizes the benefits of structured pruning and examines the results obtained with a model based on Vgg16. © 2025 ieee.

关键词： Convolution

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