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

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A Two-stage H.264 based Video compression Method for Automotive Cameras 5

A Two-stage H.264 based Video Compression Method for Automot...

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ieee 5th International conference on Industrial Cyber-Physical Systems (ICPS)

作者： Wang, Yiting Chan, Pak Hung Donzella, Valentina Univ Warwick WMG Coventry W Midlands England

ISBN: (纸本)9781665497701

With the development of automated vehicles and advanced driver assistance systems, the compression of the large amount of data generated by the vehicle camera sensors becomes a necessary processing step to improve the automated driving system efficiency. H.264 is a widely adopted video compression scheme, and it has been designed for human vision. Rate control in H.264 uses fixed quantisation parameter, however, this process can lead to fluctuation in different regions of the image quality of each frame. In this paper, we propose a two-stage H.264 based video compression framework, named "Two Stage compression (TSC)", to compress the automotive camera videos with different values of compression rate in different regions of each frame. In the first stage, each frame will be divided into the region-of-interest and the region-out-of-interest. In the second stage, different compression ratios will be applied based on the importance of the region. The experimental results show that under the same overall compression ratio, our proposed TSC increments the semantic-aware PSNR by 3.213 dB compared to uniform H.264 compression. Our method is also compared to uniform H.264 compression using a segmentation algorithm, with an improvement of 1.77% in mIOU, the average Intersection over Union.

关键词： Automotive camera data video compression semantic segmentation machine learning Intelligent Vehicles Automated and Assisted driving

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A Dynamic Codec with Adaptive Quantization for Convolution Neural Network 15

A Dynamic Codec with Adaptive Quantization for Convolution N...

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15th ieee International conference on ASIC, ASICON 2023

作者： Ouyang, Yichen Wang, Xianglong Shi, Gang Chen, Lei An, Fengwei Southern University of Science and Technology School of Microelectronics Shenzhen China

ISBN: (纸本)9798350312980

Current convolutional neural networks (CNN) have achieved a high inference accuracy by deeper architecture, generating a large amount of interlayer data. Limited to on-chip memory, massive feature maps significantly impact the performance of hardware platforms. In this paper, we propose a dynamic codec with adaptive quantization for compressing inlayer feature maps which efficiently reduces the memory storage. Discrete cosine transform (DCT) is utilized to concentrate essential information in the frequency domain while high-frequency components are compressed by adaptive quantization. The quantization tables are dynamically adjusted by the storage of the previous map and the number of the current layer to maintain a relatively high compression ratio and quality for each layer. In addition, Huffman coding and run length encoding (RLE) are used to code compressed data streams from 2-D to 1-D for storage. Furthermore, the codec is implemented on FPGA and synthesized in TSMC 28nm technology. It yields a reduction of on-chip memory by 44.17% and achieves an average compression ratio of 69.18% for feature maps at diverse depths in ResNet-50. © 2023 ieee.

关键词： adaptive quantization discrete cosine transform hardware implementation image compression

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Lossy and Lossless compression for BioFilm Optical Coherence Tomography (OCT)

Lossy and Lossless Compression for BioFilm Optical Coherence...

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2023 International conference on High Performance Computing, Network, Storage, and Analysis, SC Workshops 2023

作者： Faykus, Max Henry Calhoun, Jon Smith, Melissa Clemson University ClemsonSC United States

ISBN: (纸本)9798400707858

Optical Coherence Tomography (OCT) is a fast and non-destructive technology for bacterial biofilm imaging. However, OCT generates approximately 100 GB per flow cell, which complicates storage and data sharing. data reduction reduces data complications by reducing overhead and the amount of data transferred. This work leverages the similarities between layers of OCT images to minimize data in order to improve compression. This paper evaluates 5 lossless and 2 lossy state-of-the-art compressors as well as 2 pre-processing techniques to reduce OCT data. Reduction techniques are evaluated to determine which compressor has the most significant compression ratio while maintaining a strong bandwidth and minimal image distortion. Results show SZ with frame before pre-processing is able to achieve the highest CR of 204.6 × on its higher error bounds. The maximum compression bandwidth SZ on higher error bounds is ~41MB/s, for decompression bandwidth, it is able to outperform ZFP achieving ~67MB/s. © 2023 Owner/Author.

关键词： Optical tomography

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Image Denoising Method Based on Channel compression and Split-Attention Mechanism 5

Image Denoising Method Based on Channel Compression and Spli...

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5th International conference on Computer Engineering and Intelligent Control, ICCEIC 2024

作者： Sun, Hao Qiao, Xiaoyan School of Computer Science and Technology Shandong Technology and Business University Technology and Evaluation Shandong Engineering Research Center Yantai Key Laboratory of Big Data Modeling and Intelligent Computing Immersion Shandong Yantai China

ISBN: (纸本)9798331507992

In the image restoration task, how to make full use of spatial and channel feature information to improve the reconstruction quality of the model without significantly increasing the computational complexity is an important research issue. To address this challenge, this paper proposes a CS module (Channel compression and Split-Attention) specifically for optimizing image denoising tasks. The module first compresses the features through a preliminary convolution operation, then introduces the split attention convolution mechanism to split the features by channel and dynamically weight them, thereby enhancing the model's adaptive attention to different feature channels and improving feature expression capabilities. Through experiments on multiple image denoising datasets, the results show that the CS module significantly improves the image reconstruction quality, especially in terms of peak signal-to-noise ratio (PSNR). At the same time, the introduction of the CS module does not lead to a significant increase in computational complexity, maintaining a high computational efficiency, which verifies the practicality and superiority of the module in image denoising tasks. © 2024 ieee.

关键词： Restoration

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Accelerating Broadcast Communication with GPU compression for Deep Learning Workloads 29

Accelerating Broadcast Communication with GPU Compression fo...

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29th Annual ieee International conference on High Performance Computing, data, and Analytics (HiPC)

作者： Zhou, Qinghua Anthony, Quentin Shafi, Aamir Subramoni, Hari Panda, Dhabaleswar K. (Dk) Ohio State Univ Dept Comp Sci & Engn Columbus OH 43210 USA

ISBN: (纸本)9781665494236

With the rapidly increasing model sizes, state-ofthe-art Deep Learning (DL) models rely on multiple GPU nodes to run distributed training. Large message communication of GPU data between the GPUs is becoming a performance bottleneck in the overall training performance. GPU-Aware MPI libraries are widely adopted for state-of-the-art DL frameworks to improve communication performance. In the existing optimization solutions for Distributed data-Parallel (DDP) training, the broadcast operation is often utilized to sync up the updated model parameters among all the GPUs. However, for state-of-the-art GPU-Aware MPI libraries, broadcasting large GPU data turns to overburden the training performance due to the limited bandwidth of interconnect between the GPU nodes. On the other hand, the recent research on using GPU-based compression libraries to lower the pressure on the nearly saturated interconnection and co-designing online compression with the communication pattern provides a new perspective to optimize the performance of broadcast on modern GPU clusters. In this paper, we redesign the GPU-Aware MPI library to enable efficient collective-level online compression with an optimized chunked-chain scheme for large message broadcast communication. The proposed design is evaluated to show benefits at both microbenchmark and application levels. At the microbenchmark level, the proposed design can reduce the broadcast communication latency by up to 80.9% compared to the baseline using a state-of-the-art MPI library and 55.1% compared to the existing point-to-point-based compression on modern GPU clusters. For DDP training with PyTorch, the proposed design reduces the training time by up to 15.0% and 6.4% compared to the existing chunked-chain scheme and pointto-point-based compression, respectively, while keeping similar training accuracy. To the best of our knowledge, this is the first work that leverages online GPU-based compression techniques to significantly accelerate br

关键词： Broadcast compression GPU-Aware MPI Deep Learning

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A Time-Domain Vector Network Analyzer Suitable to Characterize Passive and Active Devices 4

A Time-Domain Vector Network Analyzer Suitable to Characteri...

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4th ieee MTT-S Latin America Microwave conference (LAMC)

作者： Villagran-Gutierrez, A. Loo-Yau, J. R. Hernandez-Dominguez, E. A. Figueroa-Resendiz, B. E. Moreno, P. Reynoso-Hernandez, J. Apolinar IPN Unidad Guadalajara Cinvestav Guadalajara Ctr Invest & Estudios Avanzados Guadalajara Jalisco Mexico Ctr Invest Cient & Educ Super Ensenada CICESE Ensenada Baja California Mexico

ISBN: (纸本)9798350316407

This work presents the implementation of a Time-Domain Vector Network Analyzer (TD-VNA) that measures the S-parameters of passive and active devices using the time-domain waveform of the incident and reflected voltages measured with an oscilloscope. The calibration of the TD-VNA is fully compatible with traditional calibration techniques used in a Vector Network Analyzer. The proposed TD-VNA can measure small and large-signal S-parameters of a transistor, as well as the transistor's gain compression and output power and the magnitude and relative phase of the transistor's output spectral components, the latter being a feature not available in commercial VNAs. Experimental data from a microstrip low-pass filter and a GaAs FET ATF34143 measured with the TD-VNA and with conventional test systems show a correlation higher than 99.5%

关键词： Time-Domain VNA S-parameters Large Signal S-parameters Gain compression Measurements

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Research on the Application of CIoU and CNN Joint compression in Autonomous Driving 6

Research on the Application of CIoU and CNN Joint Compressio...

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6th International conference on Robotics, Intelligent Control and Artificial Intelligence, RICAI 2024

作者： Liu, Xintong Gong, Gu Jiang, Fan Shang, Gongyu School of Computer Science and Technology Jiangsu Normal University Xuzhou China School of Communication and Information Engineering Xi'an University of Posts and Telecommunications Xi'an China

ISBN: (纸本)9798331541699

With the rapid development and application of autonomous driving technology, vehicle computing devices are facing increasingly daunting challenges. These systems need to process a large amount of sensory data in real-time, plan routes, and make decisions, which requires more computational resources. Therefore, effective model compression methods have become crucial. Our study aims to reduce the size and computational complexity of models through weight pruning and convolutional split compression methods, thereby reducing the consumption of computational resources while maintaining model performance and accuracy. We also utilize the CIoU loss function to improve the accuracy and efficiency of obstacle detection in autonomous driving systems. In simulated experiments, we tested and evaluated the improved model using a dataset of simulated road scenarios. The results of the experiments validate the effectiveness of model compression and demonstrate the advantages of the improved model in increasing obstacle detection rates and reducing computational costs. These improvements can help autonomous driving systems operate efficiently with limited computational resources, thereby enhancing system reliability and practicality. Our research provides valuable theoretical and practical support for advancing autonomous driving technology. © 2024 ieee.

关键词： Obstacle detectors

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Content-Aware Error Concealment in 3D Geometry compression for Interactive Virtual Reality Applications 15

Content-Aware Error Concealment in 3D Geometry Compression f...

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

作者： Ganga, S. Goyal, Shubhi Krishna, Gautham Nalawade, Dattatraya Babanrao Kuppuraj, T. Khanna, Lalit Prince Shri Venkateshwara Padmavathy Engineering College Department of Information Technology Chennai India Quantum University Research Center Quantum University India School of Engineering and Technology Department of Civil Engineering Karnataka Bangalore India Vishwakarma Institute of Information Technology Mech Engg Pune India Karpagam Academy of Higher Education Department of Computer Applications Coimbatore India Chitkara University Centre of Research Impact and Outcome Punjab Rajpura140417 India

ISBN: (纸本)9798350370249

Content-conscious mistake concealment is an emerging era that is being used to enhance the compression of 3-D geometry information for interactive digital reality (VR) programs. The purpose of this generation is to provide interactive VR packages with improved visible enjoyment by decreasing the quantity of excessive data being downloaded while simultaneously preserving the fidelity of the geometry. That is completed with the aid of using numerous techniques consisting of temporal prediction, facial animation, intensity-keeping techniques, and partial-fill strategies. The temporal prediction approach estimates future frames primarily based on the corresponding movement of formerly encoded frames with the help of temporal movement vector estimation. This technique can efficiently reduce the compression-triggered artifacts from 3D geometry facts and, hence, improve the performance of the compression set of rules. The facial animation technique, then again, is used to reduce facial animation errors and beautify the realism of the 3-D geometry. This approach employs movement-based total information compression accompanied by facial animation processes for statistics representation and information restoration after compression. This technique may be correctly used to lessen statistics redundancies due to frame synthesis and convey greater correct facial expressions. Depth-keeping techniques can correctly lessen the number of visible artifacts in compression while maintaining the depth of data. © 2024 ieee.

关键词： Concealment Efficiently Emerging Geometry Interactive Technique

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Image compression and Reconstruction Based on Quantum Network

Image Compression and Reconstruction Based on Quantum Networ...

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1st International conference on Smart Energy Systems and Artificial Intelligence (SESAI)

作者： Ji, Xun Liu, Qin Huang, Shan Chen, Andi Wu, Shengjun Nanjing Univ Sch Phys Natl Lab Solid State Microstruct Nanjing 210093 Peoples R China Nanjing Univ Collaborat Innovat Ctr Adv Microstruct Nanjing 210093 Peoples R China Nanjing Univ Inst Brain Sci Nanjing 210023 Peoples R China Nanjing Univ Kuang Yarning Honors Sch Nanjing 210023 Peoples R China Univ Sci & Technol China Hefei Natl Lab Hefei 230088 Peoples R China

ISBN: (纸本)9798350364613;9798350364606

Quantum network is an emerging type of network structure that leverages the principles of quantum mechanics to transmit and process information. Compared with classical data reconstruction algorithms, quantum networks make image reconstruction more efficient and accurate. They can also process more complex image information using fewer bits and faster parallel computing capabilities. Therefore, this paper will discuss image reconstruction methods based on our quantum network and explore their potential applications in image processing. We will introduce the basic structure of the quantum network, the process of image compression and reconstruction, and the specific parameter training method. Through this study, we can achieve a classical image reconstruction accuracy of 97.57%. Our quantum network design will introduce novel ideas and methods for image reconstruction in the future.

关键词： image compression and reconstruction quantum network parameter training

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On the Road to Portability: Compressing End-to-End Motion Planner for Autonomous Driving

On the Road to Portability: Compressing End-to-End Motion Pl...

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

作者： Feng, Kaituo Li, Changsheng Ren, Dongchun Yuan, Ye Wang, Guoren Beijing Inst Technol Beijing Peoples R China ALLRIDE AI Nanjing Jiangsu Peoples R China Hebei Prov Key Lab Big Data Sci & Intelligent Tec Tianjin Peoples R China

ISBN: (纸本)9798350353006

End-to-end motion planning models equipped with deep neural networks have shown great potential for enabling full autonomous driving. However, the oversized neural networks render them impractical for deployment on resource-constrained systems, which unavoidably requires more computational time and resources during reference. To handle this, knowledge distillation offers a promising approach that compresses models by enabling a smaller student model to learn from a larger teacher model. Nevertheless, how to apply knowledge distillation to compress motion planners has not been explored so far. In this paper, we propose PlanKD, the first knowledge distillation framework tailored for compressing end-to-end motion planners. First, considering that driving scenes are inherently complex, often containing planning-irrelevant or even noisy information, transferring such information is not beneficial for the student planner. Thus, we design an information bottleneck based strategy to only distill planning-relevant information, rather than transfer all information indiscriminately. Second, different waypoints in an output planned trajectory may hold varying degrees of importance for motion planning, where a slight deviation in certain crucial waypoints might lead to a collision. Therefore, we devise a safety-aware waypoint-attentive distillation module that as-signs adaptive weights to different waypoints based on the importance, to encourage the student to accurately mimic more crucial waypoints, thereby improving overall safety. Experiments demonstrate that our PlanKD can boost the performance of smaller planners by a large margin, and significantly reduce their reference time.

关键词： End-to-End Autonomous Driving Knowledge Distillation Model compression Motion Planning

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