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

共 16771 条记录，以下是911-920 订阅

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AdaQAT: Adaptive Bit-Width Quantization-Aware Training 6

AdaQAT: Adaptive Bit-Width Quantization-Aware Training

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6th International conference on AI Circuits and Systems (AICAS)

作者： Gernigon, Cedric Filip, Silviu-Ioan Sentieys, Olivier Coggiola, Clement Bruno, Mickael Univ Rennes INRIA CNRS IRISA F-35000 Rennes France CNES Spacecraft Tech Onboard Data Handling Toulouse France

ISBN: (纸本)9798350383638;9798350383645

Large-scale deep neural networks (DNNs) have achieved remarkable success in many application scenarios. However, high computational complexity and energy costs of modern DNNs make their deployment on edge devices challenging. Model quantization is a common approach to deal with deployment constraints, but searching for optimized bit-widths can be challenging. In this work, we present Adaptive Bit-Width Quantization Aware Training (AdaQAT), a learning-based method that automatically optimizes weight and activation signal bit-widths during training for more efficient DNN inference. We use relaxed real-valued bit-widths that are updated using a gradient descent rule, but are otherwise discretized for all quantization operations. The result is a simple and flexible QAT approach for mixed-precision uniform quantization problems. Compared to other methods that are generally designed to be run on a pretrained network, AdaQAT works well in both training from scratch and fine-tuning scenarios. Initial results on the CIFAR-10 and ImageNet datasets using ResNet20 and ResNet18 models, respectively, indicate that our method is competitive with other state-of-the-art mixed-precision quantization approaches.

关键词： Neural Network compression Quantization Aware Training Adaptive Bit-Width Optimization

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Hyper-SNBRDF: Hypernetwork for Neural BRDF Using Sinusoidal Activation 11

Hyper-SNBRDF: Hypernetwork for Neural BRDF Using Sinusoidal ...

引用

International conference in 3D Vision (3DV)

作者： Li, Zhiqiang Shen, Xukun Zhou, Xueyang Hu, Yong Li, Bowen Beihang Univ State Key Lab Virtual Real Technol & Syst Beijing Peoples R China Beihang Univ Sch New Media Art & Design Beijing Peoples R China Beihang Univ Yunnan Innovat Inst Beijing Peoples R China

ISBN: (纸本)9798350362466;9798350362459

Densely captured real-world materials require effective compression for rendering, material generation and reconstruction. Neural networks with high compression rates and the ability to fit complex functions can encode each BRDF into the corresponding network. However, current works that take advantage of single implicit neural representations are incapable of effectively modeling the high-frequency details of the highlight region. In this paper, we propose an improved compact neural network representation of BRDF data based on the sinusoidal activation. The lightweight network and the periodic activation function improve the fidelity of the reproduction material appearance under the condition of a high compression rate. Furthermore, the method of building a unified model using neural networks can decode all materials from latent space. However, the deep structure of the network model increases memory consumption. To overcome this challenge, we propose a hypernetwork framework that compresses measured BRDFs to latent space and generates weights for the neural networkbased representation of materials. The lightweight implicit representation of BRDF generated by training directly from original materials shows the characteristics of a low memory footprint and high-precision reproduction of appearance. Additionally, we apply the hypernetwork to reconstruct materials from a single image. Thanks to implicit representation of BRDF that can reproduce the appearance with high fidelity, the reflectance properties can be accurately recovered.

关键词： BRDF BRDF compression Material

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Robustness of Quantum Algorithms Against Approximate data Representations 4

Robustness of Quantum Algorithms Against Approximate Data Re...

引用

4th ieee International conference on Quantum Computing and Engineering, QCE 2023

作者： Los, Vladyslav Maksymenko, Mykola Koch-Janusz, MacIej Pryyma, Yuriy Givhan, Richard Haiqu Inc.

ISBN: (纸本)9798350343236

Quantum algorithms face practical difficulties and overheads in execution on realistic devices, potentially thwarting their theoretical advantages. A key practical challenge is efficiently encoding classical data, since data embedding layers, often exponential in the number of qubits, can consume a significant portion of the circuit depth. One possible strategy to address this is to compress the standard data embeddings using variational circuits. The approximate amplitude encoding (AAE) algorithm successfully applies this approach to financial calculations and image pattern matching, achieving only polynomially deep embedding layers. While prior works focus on optimizing the approximate embedding in isolation, the ultimate goal is the compound algorithm's performance. This study systematically examines the impact of embedding compression on the fidelity of the subsequent quantum algorithm in a noiseless setting, specifically investigating amplitude encoding and its dependence on input data set size and complexity. Trainability of the different variational ansaetze, their output state fidelity, and the degree of the embedding layer depth compression are investigated. Performance benchmarks of standard quantum algorithms, e.g. Grover search, amplitude estimation, and quantum Fourier transform (QFT), are conducted based on the degree of compression and quality of the compressed input state. Results show that the amplitude encoding circuit can be compressed substantially in depth, for generic data and a small number of qubits, with only marginal loss in state fidelity. Certain algorithms, like QFT, demonstrate robustness to such approximations. © 2023 ieee.

关键词： Embeddings

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Performance Analysis of Power data compression Based on Beidou Short Messages 6

Performance Analysis of Power Data Compression Based on Beid...

引用

6th ieee International conference on Computer and Communication Engineering Technology, CCET 2023

作者： Liwei, Ren Hui, Huang Zifei, Hao Yuwei, Wang Qiujian, Wu Zhengong, Cai Bowei, Yang State Grid Zhejiang Electric Power Company Lishui Power Company Lishui China College of Software Technology Zhejiang University Ningbo China Science and Technology Network Branch of Lishui Zhenghao Electric Power Industry Group Co. Ltd Lishui China School of Aeronautics and Astronautics Zhejiang University Hangzhou China

ISBN: (纸本)9798350340686

There are situations in the power grid information collection system in remote areas, such as weak and unstable signals. By using the Beidou short message service based on the satellite communication technology, the communication blind area and the transmission bit error rate can be reduced effectively. However, Beidou short message communication has the disadvantages of small transmission bandwidth and low communication frequency. In this paper, we focus on the IEC101 protocol message and CDT protocol message commonly used in the electricity information collection system. To address the communication challenges associated with these protocols, we propose a compression transmission mechanism based on Beidou short message, leveraging a lossless compression method. In order to maximize the utilization of BDS short message communication channel bandwidth, we design a mechanism for unpacking, grouping and reliable transmission long message within the BDS short message communication, and Huffman, LZ77, LZSS, LZW, LZMA are used for transmission segments of different lengths. compression algorithm is used for compression test, and the average compression ratio and overall transmission time are analyzed. On the basis of balancing the compression ratio and real-time transmission, the appropriate length composition of the transmission message and the compression algorithm conforming to the message characteristics are discussed. The experimental results demonstrate that the LZMA compression algorithm improves the transmission efficiency of Beidou short messages in long-distance communication seenarios. Specifically, we split IEC101 protocol packets into 900 bytes and CDT protocol packets into 800 bytes, while the transmission is real-time and the transmission efficiency of the IEC101 protocol and CDT protocol messages in the communication of the electricity information acquisition system, is increased to 2.88 times and 2.56 times respectively. © 2023 ieee.

关键词： data compression

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Research on B-spline smoothing compression and G2-continuous blending algorithm for continuous short line segment tool paths 4

Research on B-spline smoothing compression and G2-continuous...

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4th International conference on Electrical Engineering and Mechatronics Technology, ICEEMT 2024

作者： Li, Dan Xu, Jiao Li, Yi Zhou, Di Chen, Jiahao Xi'an Jiaotong University School of Instrument Science and Technology Shanxi Xi'an China Xi'an Jiaotong University School of Mechanical Engineering Shanxi Xi'an China

ISBN: (纸本)9798350356472

To address the issue of non-smooth speed and acceleration caused by tool paths represented by short line segments in CNC machining, it is necessary to use spline curves to smooth and compress these continuous short line segments. The paper employs a B-spline smoothing compression algorithm based on leading point fitting to compress the continuous short line segment tool paths, while using quintic B-splines to improve the continuity between the compressed B-splines, thereby achieving G2 continuity of the tool paths. By calculating the discrete curvature of the original path, the data is processed in segments. Each segment is compressed using B-splines according to a dominant point selection strategy. Simulation experiments show that the compression reduces the amount of program information while maintaining the allowable error. By introducing error constraints and shape-preserving constraints with a blending factor, blending curves are inserted between each B-spline segment, ensuring that the final compressed curve meets G2 continuity. Through simulation compression and interpolation experiments, comparisons were made between the curvature and speed before and after implementing the blending algorithm. The results indicate that the proposed blending algorithm reduces curvature fluctuations and improves machining efficiency. © 2024 ieee.

关键词： Splines

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Joint compression-Encryption Technique Based On Arithmetic Coding for JPEG Images 5

Joint Compression-Encryption Technique Based On Arithmetic C...

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5th International ieee Novel Intelligent and Leading Emerging Sciences conference, NILES 2023

作者： El-Arsh Mohamed, Hassan Y. Abdelaziz, Amr Elliethy, Ahmed Aly, Hussein A. Eldin Rohiem Shehata, Alaa Military Technical College Cairo Egypt

ISBN: (纸本)9798350381030

An efficient joint compression-encryption technique for JPEG images using nonlinear properties of the arithmetic coder is proposed. The technique performs both entropy coding and encryption in a single step by manipulating the probability maps of the arithmetic coder while preserving the syntax structure of the image. The proposed scheme operates on a bit-by-bit basis while taking into account the previously processed data-bits keeping the advantages of both stream and block ciphers. The proposed technique is implemented using Libjpeg-turbo. The results demonstrate that the proposed scheme attains a security level comparable to that of AES while reducing both delay and system resource requirements while maintaining the compression efficiency of the standard JPEG encoder. © 2023 ieee.

关键词： Cryptography

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Modern 3d compression Application in Medical Imaging Approach 10

Modern 3d Compression Application in Medical Imaging Approac...

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10th ieee Uttar Pradesh Section International conference on Electrical, Electronics and Computer Engineering, UPCON 2023

作者： Kanna, R. Kishore Ambikapathy, A. Al-Hameed, Mazin Riyadh Reddy, V Venkat Singh, Navdeep Jerusalem College of Engineering Department of Biomedical Engineering Chennai India Galgotias College of Engineering and Technology Department of of Electrical and Electronics Engineering India National University of Science and Technology Collage of Engineering Dhi Qar Iraq Institute of Aeronautical Engineering Hyderabad India Lovely Professional University Punjab Phagwara India

ISBN: (纸本)9798350382471

When medical pictures are compressed, valuable information is extracted from the data they contain that is crucial for clinicians to understand clinically. The purpose of the notion of image compression is to enhance the picture content by compressing two images, such as MRI and CT scans, in order to provide doctors accurate and helpful information for their clinical care. In this project, two medical photos were combined using the Discrete Wavelet Transforms (DWT) approach to separate the functional and anatomical images. The compressed picture has no color alterations and both additional spatial attributes and functional information. According to experimental data, discrete wavelet transformations provide the highest compression performance. © 2023 ieee.

关键词： CT.DWT. Medical Image MRI

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Multi-Rate Adaptive Transform Coding for Video compression 48

Multi-Rate Adaptive Transform Coding for Video Compression

引用

48th ieee International conference on Acoustics, Speech and Signal Processing, ICASSP 2023

作者： Duong, Lyndon R. Li, Bohan Chen, Cheng Han, Jingning Nyu Center for Neural Science New YorkNY United States Open Codecs Google Llc Mountain ViewCA United States

ISBN: (纸本)9781728163277

Contemporary lossy image and video coding standards rely on transform coding, the process through which pixels are mapped to an alternative representation to facilitate efficient data compression. Despite impressive performance of end-to-end optimized compression with deep neural networks, the high computational and space demands of these models has prevented them from superseding the relatively simple transform coding found in conventional video codecs. In this study, we propose learned transforms and entropy coding that may either serve as (non)linear drop-in replacements, or enhancements for linear transforms in existing codecs. These transforms can be multi-rate, allowing a single model to operate along the entire rate-distortion curve. To demonstrate the utility of our framework, we augmented the DCT with learned quantization matrices and adaptive entropy coding to compress intra-frame AV1 block prediction residuals. We report substantial BD-rate and perceptual quality improvements over more complex nonlinear transforms at a fraction of the computational cost. © 2023 ieee.

关键词： entropy coding transform coding video compression

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On Pre-chewing compression Degradation for Learned Video compression

On Pre-chewing Compression Degradation for Learned Video Com...

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ieee International conference on Visual Communications and Image Processing (VCIP)

作者： Ho, Man Minh Sun, Heming Zhang, Zhiqiang Zhou, Jinjia Hosei Univ Tokyo Japan Waseda Univ Tokyo Japan JST PRESTO Tokyo Japan

ISBN: (纸本)9781665475921

Artificial Intelligence (AI) needs huge amounts of data, and so does Learned Restoration for Video compression. There are two main problems regarding training data. 1) Preparing training compression degradation using a video codec (e.g., Versatile Video Coding - VVC) costs a considerable resource. Significantly, the more Quantization Parameters (QPs) we compress with, the more coding time and storage are required. 2) The common way of training a newly initialized Restoration Network on pure compression degradation at the beginning is not effective. To solve these problems, we propose a Degradation Network to pre-chew (generalize and learn to synthesize) the real compression degradation, then present a hybrid training scheme that allows a Restoration Network to be trained on unlimited videos without compression. Concretely, we propose a QP-wise Degradation Network to learn how to compress video frames like VVC in real-time and can transform the degradation output between QPs linearly. The real compression degradation is thus pre-chewed as our Degradation Network can synthesize the more generalized degradation for a newly initialized Restoration Network to learn easier. To diversify training video content without compression and avoid overfitting, we design a Training Framework for Semi-compression Degradation (TF-SCD) to train our model on many fake compressed videos together with real compressed videos. As a result, the Restoration Network can quickly jump to the near-best optimum at the beginning of training, proving our promising scheme of using pre-chewed data for the very first steps of training. In other words, a newly initialized Learned Video compression can be warmed up efficiently but effectively with our pre-trained Degradation Network. Besides, our proposed TF-SCD can further enhance the restoration performance in a specific range of QPs and provide a better generalization about QPs compared with the common way of training a restoration model. Our work is

关键词： Deep Learning Video compression compression Degradation Semi-Supervised Learning

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A Comparative Analysis on 1D & 2D ECG applying data compression Approaches

A Comparative Analysis on 1D & 2D ECG applying Data Compress...

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2023 International conference on Artificial Intelligence and Smart Communication, AISC 2023

作者： Tripathi, Pragati Ansari, M.A. Kumar Gandhi, Tapan Mehrotra, Rajat Layba, L. Gupta, Satyam Junaid, Mohd Gautam Buddha University Department of Electrical Engineering Greater Noida India I.I.T. Delhi Department of Electrical Engineering India G.L. Bajaj Institute of Technology & Management Department of Electrical Engineering India

ISBN: (纸本)9798350322309

The valuation of ECG signal quality after compression is an important task to be performed for the compression process. compression enables the signal achieving, accelerates and decreases the energy ingesting. In contrary, lossy compression approaches pursuance by both compression efficacy and quality of signal. The manuscript gives an overview of the objective algorithms for the valuation of both ECG signal quality. The paper gives an idea of the latest algorithms for the valuation of ECG signal quality after compression approaches and efficiency of the signal. In this domain there is the absence of extensive review. Electrocardiogram (ECG) data compression plays a significant role in localized digital storage or efficient communication channel utilization in telemedicine applications. Based on domain ECG data compression can be performed either one dimensional (1D) or two dimensional (2D) for utilization of inter and inter with intra beat correlation respectively. In this paper, a comparative study between 1D & 2D ECG data compression methods were taken out from existing literature to provide an update in this regard. ECG data compression techniques and algorithms in 1D & 2D domain have their own merits and limitations. Recently, numerous research and techniques in 1D ECG data compression have been developed including direct and transformed domain. Additionally, 2D ECG data compression research is reported based on period normalization and complexity sorting in recent times. © 2023 ieee.

关键词： Electrocardiograms

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