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

共 16713 条记录，以下是101-110 订阅

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Reversible data Hiding in Encrypted Images Based on Variational Lossless compression and Dual Encryption

Reversible Data Hiding in Encrypted Images Based on Variatio...

引用

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

作者： Yaolin Yang Hongjie He School of Information Science and Technology Southwest Jiaotong University Chengdu China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

To enhance the embedding capacity and security of reversible data hiding in encrypted images (RDHEI), an RDHEI algorithm based on variational lossless compression (VLC) and dual encryption is proposed. Firstly, VLC model is introduced to ensure reversibility while achieving high embedding capacity. On the basis of variational autoencoder model, the VLC model incorporates an error encoding module to record the difference between the input and output images, thereby achieving lossless compression. Subsequently, dual encryption includes both stream encryption and model encryption, ensuring security both in the carrier and method, and original images necessitate dual decryption for accurate recovery. Experimental results demonstrate that the proposed algorithm elevates embedding rate by at least 0.12 bpp in UCID compared to existing algorithms, and can resist brute force attacks to achieve high security.

关键词： Resistance Image coding Signal processing algorithms Resists Receivers Streaming media Signal processing Brute force attacks Encryption Speech processing

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TD-dAJC: A 2pJ/Pixel Time-Domain Weight and Integrating-MAC Based Direct-Analog-to-MJPEG compression for Video Sensor Nodes

TD-dAJC: A 2pJ/Pixel Time-Domain Weight and Integrating-MAC ...

引用

Custom Integrated Circuits conference (CICC)

作者： Gourab Barik Harshit Naman Yudhajit Ray Shreyas Sen Purdue University USA

ISBN: (数字)9798331517458

ISBN: (纸本)9798331517465

The increase in IoT applications has made cameras ubiquitous in various fields spanning from healthcare and autonomous vehicles to energy-constrained applications like battery-powered wearables. This proliferation has created a need for low-power camera modules for resource-constrained IoT nodes/edge devices. As the quality of images improves, these cameras produce a data deluge. Standard digital cameras generate data in the order of ~Gbps, which requires compressing the data within the camera module (e.g., SONY IMX 317) using lossy compression schemes like MJPEG or H.264. For instance, a 4K 12fps video generates around ~1 Gbps of data, which can be compressed to ~50 Mbps using MJPEG. Compared to H.264, MJPEG has lower latency and lower complexity, making it suitable for real-time edge applications. Standard digital data compression suffers from 1) high ADC energy consumption digitizing all samples, 2) Intermediate data storage requirements, and 3) huge computation power to compress the data, making it less desirable for edge cameras. The emergence of low-power camera imagers [1], such as the STM VD55G1 with power consumption as low as 3-10mW, has increased the demand for efficient compression techniques that minimize computational power as shown in Fig. 1(a) and (b).

关键词： Image coding Power demand Image edge detection Streaming media Cameras Real-time systems Internet of Things Wearable devices Time-domain analysis Standards

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Handheld AI CPR Assistive Device with Location Tracking Capability

Handheld AI CPR Assistive Device with Location Tracking Capa...

引用

Consumer Communications and Networking conference, CCNC ieee

作者： Hyun-Woong Choo Dae-Ho Kim Jae-Young Pyun Dept. of Info. and Comm. Engineering Chosun University Korea IT Research Institute Chosun University Korea

This paper proposes a handheld AI CPR assitive device estimating chest compression depth with location tracking capability. By using a model of Convolutional Neural Networks (CNN) combined with Long Short-Term Memory ... 详细信息

ISBN: (数字)9798331508050

ISBN: (纸本)9798331508067

关键词： Accelerometers Performance evaluation data models Convolutional neural networks Assistive devices Artificial intelligence Long short term memory

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A Hierarchical compression Technique for 3D Gaussian Splatting compression

A Hierarchical Compression Technique for 3D Gaussian Splatti...

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

作者： He Huang Wenjie Huang Qi Yang Yiling Xu Zhu Li Shanghai Jiao Tong University University of Missouri-Kansas City

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

3D Gaussian Splatting (GS) demonstrates excellent rendering quality and generation speed in novel view synthesis. However, substantial data size poses challenges for storage and transmission, making 3D GS compression an essential technology. Current 3D GS compression research primarily focuses on developing more compact scene representations, such as converting explicit 3D GS data into implicit forms. In contrast, compression of the GS data itself has hardly been explored. To address this gap, we propose a Hierarchical GS compression (HGSC) technique. Initially, we prune unimportant Gaussians based on importance scores derived from both global and local significance, effectively reducing redundancy while maintaining visual quality. An Octree structure is used to compress 3D positions. Based on the 3D GS Octree, we implement a hierarchical attribute compression strategy by employing a KD-tree to partition the 3D GS into multiple blocks. We apply farthest point sampling to select anchor primitives within each block and others as non-anchor primitives with varying Levels of Details (LoDs). Anchor primitives serve as reference points for predicting non-anchor primitives across different LoDs to reduce spatial redundancy. For anchor primitives, we use the region adaptive hierarchical transform to achieve near-lossless compression of various attributes. For non-anchor primitives, each is predicted based on the k-nearest anchor primitives. To further minimize prediction errors, the reconstructed LoD and anchor primitives are combined to form new anchor primitives to predict the next LoD. Our method notably achieves superior compression quality and a significant data size reduction of over 4.5× compared to the state-of-the-art compression method on small scenes datasets.

关键词： Visualization Three-dimensional displays Octrees Termination of employment Transforms Signal processing Rendering (computer graphics) Acoustics Time complexity Speech processing

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LNeRV: Learnable Hierarchical Encoding Improve Neural Representation Video Codec

LNeRV: Learnable Hierarchical Encoding Improve Neural Repres...

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

作者： Jiahong Chen Xiang Liu Bin Chen Baoyi An Dai Tao Shu-Tao Xia Singhua Shenzhen International Graduate School Tsinghua University Peng Cheng Laboratory Harbin Institute of Technology Shenzhen Huawei Technologies Co. Ltd Shenzhen University

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Existing Implicit Neural Representation (INR) video compression techniques have opened up new avenues in the field of video compression. NeRV maps the temporal coordinates to high-resolution images using neural networks, providing a more flexible and efficient encoding method for video data. However, NeRV implicitly stores all video information in the network, requiring post network compression techniques such as pruning. To integrate explicit compression and implicit representation into an end-to-end framework, this study proposes a novel neural representation-based video compression paradigm called Latent code based Neural representation video compression (LNeRV). Specifically, LNeRV consists of hierarchy feature grids, synthesis network and entropy coding network. With a single-stage training process, LNeRV achieves video compression and dynamically allocates bits according to video complexity, better fitting dynamic videos. We provide a comprehensive compression-to-decompression workflow for our approach. Extensive experimental results verify the effectiveness of our LNeRV.

关键词： Training Image coding Neural networks Rate-distortion Video compression Signal processing Dynamic scheduling Entropy coding Video codecs Speech processing

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Haptic Encoder and data compression of Multimodal data Traffic in the Tactile Internet

Haptic Encoder and Data Compression of Multimodal Data Traff...

引用

Consumer Technology (ICCT-Pacific), International conference on

作者： Stefan Aust Junichi Yamaguchi Strategic Technologies Development Group Incubation Division NEC Communication Systems Ltd. Kawasaki Kanagawa Japan Business Incubation Group Incubation Division NEC Communication Systems Ltd. Kawasaki Kanagawa Japan

ISBN: (数字)9798331504120

ISBN: (纸本)9798331504137

The efficient transmission of haptic, audio, and video (HAV) data in the Tactile Internet (TI) is the bases for successful tele-operation services over long-rage area networks. The ieee P1918.1.1 Working Group is going to standardize the encoding of haptic data in such networks. This study presents a first implementation of the haptic encoder that is in compliance with the future ieee 1918.1.1 standard for non-delay transmissions. The encoder's details are outlined and the haptic encoding performance is discussed. In a basic leader-follower system configuration, a significant encoding gain at 44 % was observed when haptic devices are idle. Hence, a haptic tele-operation system can be realized with superior efficiency when haptic devices are in idle state, so that data traffic is optimized while reducing operational network costs.

关键词： Tactile Internet Costs Sensitivity Encoding Sensor systems Haptic interfaces Sensors Quality of experience Standards Testing

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Hybrid Beamforming Optimization in Millimeter Wave MIMO Using Deep Deterministic Policy Gradient Based Deep Reinforcement Learning 3

Hybrid Beamforming Optimization in Millimeter Wave MIMO Usin...

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3rd International conference on Integrated Circuits and Communication Systems, ICICACS 2025

作者： Riadhusin, Rame Sudhakar, K. Prakash, D. Bhanu Kumar, S. Senthil Rajasubha, J. College of Technical Engineering The Islamic University Department of Computers Techniques Engineering Najaf Iraq Nitte Meenakshi Institute of Technology Department of Artificial Intelligence and Data Science Bengaluru India Cvr College of Engineering Department of Electronics and Communication Engineering Hyderabad India New Prince Shri Bhavani College of Engineering and Technology Department of Electrical and Electronics Engineering Chennai India Dhanalakshmi Srinivasan College of Engineering Technology Department of Computer Science and Engineering Mamallapuram India

ISBN: (纸本)9798331508456

In recent years, the rapid growth of Hybrid Beamforming (HB) technology has developed rapidly to improve high spectrum efficiency and data transmission in wireless communication systems. Massive Multiple Input Multiple Output (MIMO) systems are integrated with HB to encourage wireless network performance by improving signal quality. However, combination of digital and analog beamforming increases hardware complexity and quantization errors in multiple streams. Hence, this research proposes Deep Reinforcement Learning (DRL)-based hybrid beamforming optimization framework to learn relevant Channel State Information (CSI) by learning complex non-linear relationships and beam forming weights dynamically. Initially, Deep Multiple Input Multiple Output (Deep MIMO) dataset which consists of channel measurements and capacity to receive and transmit signal processing is used. After that, preprocessing is performed using Amplitude Scaling (AS) for signal compression and Phase Normalization (PN) to reduce phase noise in multiple frequencies. Finally, Deep Deterministic Policy Gradient (DDPG)-based DRL model is used to perform HB optimization through adaptive learning policy by improving sum rate and spectral efficiency while reducing quantization errors. Proposed method results with the Sum-rate (5.35 bps/HZ), Accuracy (99.26%) and Inference time (0.03 sec) when compared with existing CNN-Bi-directional Long Short-Term Memory (CNNBiLSTM). © 2025 ieee.

关键词： Beamforming

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Markerless Registration and Visualization of Volumetric Medical data for Enhanced Surgical Precision Using Head-Mounted Displays

Markerless Registration and Visualization of Volumetric Medi...

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Virtual Reality and 3D User Interfaces Abstracts and Workshops (VRW), ieee conference on

作者： Artur Jurgas Michał Trojak Maciej Stanuch Andrzej Skalski AGH University of Krakow MedApp S.A

ISBN: (数字)9798331514846

ISBN: (纸本)9798331525637

In surgery, accurate localization of critical structures like blood vessels is crucial, yet current 2D visualization methods limit precision and increase cognitive load for surgeons. This paper introduces a markerless registration approach that uses only sensors in a Head-Mounted Display to align CT/MRI data directly onto the patient in a 3D space. Point clouds extracted from CT/MRI data are registered with world captures from the Head-Mounted Display’s time-of-flight camera, allowing for precise visualization. We evaluated the system using physical measurements on phantoms and compared algorithmic registration to manual alignment. The results show a registration error of approx. 4.87±0.05mm, demonstrating the potential of this technique for preoperative planning and intraoperative support.

关键词： Point cloud compression Three-dimensional displays Head-mounted displays Navigation Surgery data visualization Phantoms Mixed reality Sensors Planning

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Neural Video Representation for Redundancy Reduction and Consistency Preservation

Neural Video Representation for Redundancy Reduction and Con...

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ieee International conference on Consumer Electronics (ICCE)

作者： Taiga Hayami Takahiro Shindo Shunsuke Akamatsu Hiroshi Watanabe Graduate School of FSE Waseda University Tokyo Japan Columbia Engineering Columbia University New York USA

ISBN: (数字)9798331521165

ISBN: (纸本)9798331521172

Implicit neural representation (INR) embed various signals into neural networks. They have gained attention in recent years because of their versatility in handling diverse signal types. In the context of video, INR achieves video compression by embedding video signals directly into networks and compressing them. Conventional methods use either frame timestamps or features extracted from individual frames as network inputs. The latter method provides greater expressive capability as the input is specific to each video. However, the features extracted from frames often contain redundancy, which contradicts the purpose of video compression. Additionally, such redundancies make it challenging to accurately reconstruct high-frequency components in the frames. To address these problems, we focus on separating the high-frequency and low-frequency components of the reconstructed frame. We propose a video representation method that generates both the high-frequency and low-frequency components of the frame, using features extracted from the high-frequency components and temporal information, respectively. This approach improves the reconstruction quality of the high-frequency components and enhances the temporal consistency of the frames. Experimental results demonstrate that our method outperforms the existing HNeRV method, achieving superior results in 96 percent of the videos.

关键词： Redundancy Video sequences Neural networks Video compression Streaming media Feature extraction data mining Streams Consumer electronics

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Compressing a Flow-Based Privacy Protection Model via a Novel Joint Distilling and Pruning Method

Compressing a Flow-Based Privacy Protection Model via a Nove...

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

作者： Sissi Xiaoxiao Wu Zhicong Liang Zehong Huang School of EIE Shenzhen Univeristy

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

While reversible anonymization techniques have been shown to be effective in protecting facial information, most of these methods rely on large reversible generative models that are only suitable for running on cloud servers, which poses a huge challenge to placing such privacy-preserving models on camera terminals. This work proposes a novel compression method that combines knowledge distillation and model pruning for training compact reversible generative models with an encoder-decoder pair. The proposed compression method exploits a novel collaborative knowledge distillation component to transfer the teacher model’s knowledge to the student model by pairing a teacher encoder (decoder) with a student decoder (encoder). Sub-sequently, a correlation-based regularized pruning component is adopted to further remove redundant parameters in the manually designed student model. This set of schemes enables us to install an anonymization system that builds privacy for sensitive personal data on embedded devices. We deploy and evaluate our compression privacy-preserving system on an NVIDIA Jetson TX2 device, which runs in real time and effectively anonymizes facial images to preserve privacy.

关键词： Training data privacy Privacy Image coding Signal processing Information filtering Decoding Servers Speech processing Information integrity

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