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Compression and Transmission of 8K Stereoscopic VR Using VAE-GAN Latents and Standard Encoders

Compression and Transmission of 8K Stereoscopic VR Using VAE...

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IEEE conference on Wireless communications and Networking

作者： Sampreet Vaidya Hatem Abou-Zeid Diwakar Krishnamurthy Department of Electrical and Software Engineering University of Calgary Calgary AB Canada

ISBN: (数字)9798350368369

ISBN: (纸本)9798350368376

Despite the heightened popularity of Virtual Re-ality (VR), streaming high-resolution stereoscopic VR remains a challenge. This is primarily due to significant bandwidth demands of high-definition VR content. While advanced deep neural networks (DNN s) have demonstrated the potential to outperform standard codecs, their integration into real-world transmission frameworks is complex and not directly compatible with current encoding standards. To bridge this gap, this paper proposes a novel technique for compressing and transmitting 8K stereoscopic scenes using Variational Autoencoder (VAE) GAN latents represented as 3-channel RGB scenes that can be transmitted via standard encoders. The proposed method reduces bandwidth requirements by 45.1 % across different 8K scenes while maintaining visual quality, highlighting the effectiveness of the approach. This study also investigates the impact of varying patch-sizes of input frames for model training and evaluate its influence on client-side reconstructions. We then explore various transmission configurations of latent frames. Our findings suggest that while residual transmission offers limited benefits for 3-channel latent frame compression, raw transmission consistently yields better results, particularly for texture-heavy scenes. To the best of our knowledge, this is the first such transmission study on 8K stereoscopic scenes for cloud-based VR, providing valuable insights for optimizing high-resolution VR streaming systems. Code: ***/sampreetucalgary07/8K-VR-compression.

关键词： Training Cloud computing visualization Solid modeling Stereo image processing Bandwidth Virtual reality Streaming media Generative adversarial networks Standards

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3rd International conference on Electronic Governance with Emerging Technologies, EGETC 2024

3rd International Conference on Electronic Governance with E...

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3rd International conference on Electronic Governance with Emerging Technologies, EGETC 2024

ISBN: (纸本)9783031770289

The proceedings contain 17 papers. The special focus in this conference is on Electronic Governance with Emerging Technologies. The topics include: Ecological Development of a Novel Machine Learning Technique for Electricity Consumption and Cost Prediction in Cloud Computing;blockchain Based Decentralized Approach for Securing Fog Computing Environment;Enhancing Face Spoofing Detection Via CNN Model Integration with Normalized Features;Study of ZigBee-Based Communication to Share Operative Weather Information Between UASs in the Frame of Smart Cities Concept;automating Exam Assessment Through Digital Transformation and image processing;an Analysis of Ethical Dimension of Machine Learning Models in Software Defect Prediction in Healthcare Sector;leaf Diseases Detection in Peach Using EfficientNet;medInsight: A Personalized Prescription Recommendation Using Machine Learning;intelligent Machine Learning for Cybersecurity: Anomaly Detection in Network Intrusion Systems and Beyond;Enhancing Cyber Resilience: Implementing DFMCAI for Threat Detection and Analysis;forecasting Future Credit Card Delinquencies: Leveraging Data Tables and visual Analysis for Enhanced Accuracy;navigating the Strategic Landscape: An In-Depth Review of the Strategic Planning Process;exploring Text Vectorization on the Polarity Detection of Spanish Comments;Evolutionary Optimization Techniques for the Design of Three-Dimensional UAV’s Antenna Arrays;cooperative Multi-agent Pursuit-Evasion Using Convolutional Neural Networks.

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Residential Real Estate image Classification for Property Valuation

Residential Real Estate Image Classification for Property Va...

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28th International conference on image processing, Computer Vision, and Pattern Recognition, IPCV 2024, and 23rd International conference on Information and Knowledge Engineering, IKE 2024, held as part of the World Congress in Computer Science, Computer Engineering and Applied Computing, CSCE 2024

作者： Nejad, Mehrdad Ziaee Naderpour, Mohsen Behbood, Vahid Ramezani, Fahimeh Lu, Jie 15 Broadway UltimoNSW2007 Australia

ISBN: (纸本)9783031859328

Residential real estate price is one of the key components of our economic developments and has also been a major concern of the public, bank industry, government, and investors. The accurate estimation of the sale price and its changes have an important role in the decision-making of related departments and organizations. In Australia, one of the biggest investments for people is in residential real estate. Therefore, many studies and research works have been carried out to build an automated valuation model to predict sale prices of residential properties accurately as much as possible. Automatic and accurate image classification of residential real estate plays an important role in property valuation and decision making of both sellers and buyers. It can be used in real estate online websites to organize the images for each property or used as a component in a visual decision support system for predicting the property sale prices based on property images. As convolutional image classification models show valuable performance in comparison with traditional models, a convolutional classification model is developed in this paper which creates a highly reliable classification component to be used in the corresponding research areas. The performance of the proposed model is investigated through a real dataset of New Sales Wales, Australia. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Convolutional neural networks

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Detecting Emotions of MEMEs Using a Hybrid Approach of Deep Learning

Detecting Emotions of MEMEs Using a Hybrid Approach of Deep ...

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3rd International conference on Advancements in Smart Computing and Information Security, ASCIS 2024

作者： Patel, Monali Diwanji, Hiteishi L.J University Gujarat Ahmedabad India L.D Engineering College Gujarat Ahmedabad India

ISBN: (纸本)9783031862892

Memes have evolved into a powerful tool for social interaction on platforms like Twitter, Instagram, Facebook, Pinterest, where they communicate complex emotions through a blend of images, text, and emojis. In this research, we propose a hybrid deep learning model that not only processes the textual components but also integrates the expressive use of emojis to better capture the nuanced emotions embedded in memes. While traditional sentiment analysis approaches often fall short in understanding these intricate emotional cues, our deep learning approach aims to overcome these challenges. By leveraging multimodal features—the textual content alongside visual cues like emojis. We provide a more holistic method for detecting and classifying emotions in memes, enhancing the accuracy of sentiment detection. Our research also distinguishes itself by focusing specifically on harmful, offensive, and trolling content, using hybrid deep learning models that integrate both Natural Language processing (NLP) techniques and image recognition. This approach not only enables the system to detect sentiment but also classifies different types of toxic behavior (like trolling) that are prevalent in meme culture. The dataset used for experimentation contains a range of memes annotated with these emotional and behavioral labels. In sum, this study contributes to the existing literature by presenting an advanced, context-aware method for meme classification, emphasizing the importance of both text and visual elements. The experiments conducted showcase the effectiveness of our model in accurately detecting complex emotional expressions, particularly in memes designed to provoke or offend. This research pushes the boundaries of meme analysis, helping to mitigate online toxicity while providing new tools for sentiment analysis. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： context-aware method Deep learning emotion detection Multimodal features Natural Language processing

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Accelerating Traditional Object Detection Methods on Sophisticated Embedded Systems 4th

Accelerating Traditional Object Detection Methods on Sophis...

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4th International conference on Intelligent Systems and Pattern Recognition, ISPR 2024

作者： Sehairi, Kamal Bouhafs, Abdelkader Boucherit, Ibtissam Mama Bouwmans, Thierry Chouireb, Fatima Department of Physics Laboratory of Applied Sciences and Didactics École Normale Supérieure de Laghouat Laghouat Algeria Department of Electronics Telecommunication Signals and Systems Laboratory University Amar Telidji of Laghouat Laghouat Algeria Department of Computer Science Lab. Mathématiques Image et Applications Université de La Rochelle La Rochelle France

ISBN: (纸本)9783031821523

Identifying and locating objects in images and videos, including elements like traffic signs, vehicles, buildings, and people, constitutes a fundamental and demanding task in computer vision, known as object detection. Due to the higher computing complexity of this technique and the large amount of data carried by the video signal, it is nearly impossible for ordinary general-purpose processors GPPs or CPUs to run these techniques in real-time, especially for embedded systems applications. Therefore, special hardware that can acquire, control, or execute in parallel is required. These specialized hardware systems include Digital Signal Processors DSPs, Field Programmable Gate Arrays FPGAs, visual processing Units VPUs, Tensor processing Units TPUs, Neural processing Units NPUs or Graphics processing Units GPUs. This work presents the benefits of accelerating traditional object detection methods on a high-end embedded system, the Jetson Nano Developer Kit. This single computer board is equipped with the Tegra K1 System on Chip SoC, which is composed of a quad-core ARM A15 and 192 cores of Kepler-embedded GPU. Computing acceleration was ensured via the use of the CUDA OpenCV library for both the Histogram of Oriented Gradients HOG and the Haar Cascade Classifier. For VGA resolution, results reveal that the GPU implementation on this embedded system is 1.4× faster than the CPU for the HOG method and 2× for the Haar Cascade Classifier method. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： System-on-chip

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MKD-YOLO: Multi-Scale and Knowledge-Distilling YOLO for Efficient PPE Compliance Detection

MKD-YOLO: Multi-Scale and Knowledge-Distilling YOLO for Effi...

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

作者： Juntao Zan Yang Fang Qilie Liu Uswah Khairuddin Yan Li Kaiwei Sun School of Communications and Information Engineering Chongqing University of Posts and Telecommunications Chongqing China Chongqing Key Laboratory of Image Cognition Chongqing University of Posts and Telecommunications Chongqing China Malaysia-Japan International Institute of Technology University of Technology Malaysia Kuala Lumpur Malaysia Department of Electrical and Computer Engineering Inha University Incheon South Korea Key Laboratory of Data Engineering and Visual Computing Chongqing University of Posts and Telecommunications Chongqing China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

YOLO-based models are widely used for personal protective equipment (PPE) compliance detection due to their excellent detection performance and efficiency. However, most YOLO models are not competent for detection tasks in complex industrial scenarios such as remote surveillance and extremely small targets. In addition, there is a lack of effective model lightweighting and knowledge transfer approaches for industrial deployment. To this end, this paper proposes a Multi-scale and Knowledge-Distilling YOLO (MKD-YOLO) based on YOLOv8n for efficient PPE compliance detection. Specifically, in backbone stage, we design an Efficient Multi-Scale Enhanced Convolution (C2f-EMSEC) module and Large Spatial Pyramid Pooling-Fast (LSPPF) module for multi-scale and global-contextual feature learning as well as reducing model complexity. Then, in neck stage, a refined Bidirectional feature Pyramid Network (BPNet) is designated to capture fine-grained details for extremely small object detection. Moreover, we apply channel-wise knowledge distillation to facilitate model lightweighting and domain-specific knowledge transfer learning. Experiments on our proposed dataset and public datasets show that the proposed MKD-YOLO achieves a new state-of-the-art (SOTA) detection performance and efficiency for practical PPE compliance detection tasks. Codes and the dataset are available at https://***/z1Zjt/MKD-YOLO.

关键词： Personal protective equipment YOLO Representation learning Convolution Surveillance Speech enhancement Feature extraction Neck Kernel Knowledge transfer

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Proceedings of SPIE - visual communications and image processing 2008

Proceedings of SPIE - Visual Communications and Image Proces...

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visual communications and image processing 2008

ISBN: (纸本)9780819469946

The proceedings contain 83 papers. The topics discussed include: measuring the Hurst parameter of compressed video sequences;coalition-based multimedia peer matching strategies for P2P networks;risk-aware scheduling multi-user video streaming multi-hop networks;video multicast over wireless mesh networks with scalable video coding (SVC);rate-distortion optimized multimedia communication in networks;distributed fine grain adaptive-FEC scheme for scalable video streaming;motion-compensated noise estimation fro effective video processing;motion tracking with non-stationary camera based on area and level set weighted average of centroid shifting vectors;a novel approach to skip mode decision for H.264;distributed pose estimation from multiple views;and smooth extraction of SVC fine-granular SNR scalable videos with a virtual-GOP-based rate distortion modeling.

关键词： image coding

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A 3D MRI Brain image Segmentation and Reconstruction System Based on Augmented Reality Technology 10th

A 3D MRI Brain Image Segmentation and Reconstruction System ...

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10th China Health Information processing conference, CHIP 2024

作者： Zhao, Wang Lu, Peixin Hu, LianTing Lu, Long School of Management Engineering Henan University of Engineering Zhengzhou China School of Safety Science and Emergency Management Wuhan University of Technology Wuhan China Guangdong Provincial People’s Hospital Guangzhou China School of Information Management Wuhan University Wuhan China The Center for Healthcare Big Data Research The Big Data Institute Wuhan University Wuhan China Institute of Pediatrics Guangzhou Women and Children’s Medical Center Guangzhou Medical University Guangzhou China School of Public Health Wuhan University Wuhan China

ISBN: (纸本)9789819637546

Background With the rapid development of information technology and the digitization of medical devices, various diseases require the use of medical imaging equipment for diagnosis. At present, various medical imaging diagnostic equipment such as CT and nuclear magnetic resonance can provide two-dimensional planar images of diseases. Doctors urgently need to accurately determine the spatial location, size, geometry, and spatial relationship with the surrounding tissue. Therefore, it is very important to use computer technology to segment 3D MRI images, determine the location of lesions, and then perform 3D reconstruction. Method At present, automatic recognition and marking of brain images are displayed in two dimensions. Therefore, it is necessary to use 3D visualization technology for reconstruction. In addition, it can be combined with virtual and real, and some additional information is superimposed on the brain image for integrated display. In addition, a combination of virtual and real needs to be superimposed, and some additional information is superimposed on the brain image for integrated display. The research focus of this paper includes two main parts: disease segmentation and 3D reconstruction visualization. Firstly, the disease segmentation method based on 3D MRI brain image files was designed, and then the feature extraction and 3D reconstruction functions were designed. Thereby forming a complete process of disease region segmentation and three-dimensional reconstruction. Results This study is based on a three-dimensional MRI brain image segmentation algorithm. The algorithm is advanced in technology, high in accuracy, and can effectively identify the location of the disease. Then, this study used the Unity tool to implement a three-dimensional reconstruction and visual display program for brain image disease segmentation. Therefore, the doctor can quickly and intuitively grasp the spatial information inside the brain and the information of the lesion

关键词： image segmentation

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Optical, analog and digital domain architectural considerations for visual communications

Optical, analog and digital domain architectural considerati...

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conference on visual communications and image processing 2008

作者： Metz, W. A. Texas Instruments Inc Dallas TX 75243 USA

ISBN: (纸本)9780819469946

The end of the performance entitlement historically achieved by classic scaling of CMOS devices is within sight, driven ultimately by fundamental limits. Performance entitlements predicted by classic CMOS scaling have progressively failed to be realized in recent process generations due to excessive leakage, increasing interconnect delays and scaling of gate dielectrics. Prior to reaching fundamental limits, trends in technology, architecture and economics will pressure the industry to adopt new paradigms. A likely response is to repartition system functions away from digital implementations and into new architectures. Future architectures for visual communications will require extending the implementation into the optical and analog processing domains. The fundamental properties of these domains will in turn give rise to new architectural concepts. The limits of CMOS scaling and impact on architectures will be briefly reviewed. Alternative approaches in the optical, electronic and analog domains will then be examined for advantages, architectural impact and drawbacks.

关键词： image systems architecture optical computing analog computing image processing CMOS limits

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image processing assisted voltage overscaling for energy efficient IC realization of motion estimation

Image processing assisted voltage overscaling for energy eff...

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conference on visual communications and image processing 2008

作者： Dani, Rajat Zhang, Tong Woods, John W. Rensselaer Polytech Inst ECSE Dept Troy NY 12181 USA

ISBN: (纸本)9780819469946

This paper presents an energy efficient VLSI architecture for motion estimation using image processing assisted voltage overscaling (VOS). Motion estimation is the most computationally expensive block inside any video encoder, typically consuming 40-60% of the total power. This work focuses on using VOS to reduce power consumption at the expense of marginal loss of visual quality. Some image processing techniques are used to assist VOS so that a better trade-off between power and visual quality can be achieved. The design is demonstrated using full search and three step search algorithms. Simulation results in 65mn CMOS technology show that the proposed technique can save up to 30% power at the cost of 0.5dB loss of PSNR.

关键词： video coding motion estimation low power

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