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

共 16713 条记录，以下是111-120 订阅

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Electrooculogram compression Based on Wavelet Packet Decomposition

Electrooculogram Compression Based on Wavelet Packet Decompo...

引用

ieee International Instrumentation and Measurement Technology conference (I2MTC)

作者： Lopez, Alberto Qaisar, Saeed Mian Ferrero, Francisco Yahiaoui, Reda Univ Oviedo Dept Elect Elect Commun & Syst Engn Gijon 33204 Spain Effat Univ Elect & Comp Engn Dept Jeddah 21478 Saudi Arabia Univ Franche Comte SINERGIE Lab F-25000 Besancon France

ISBN: (纸本)9798350380903;9798350380910

The electrooculogram (EOG) represents the electrical activity of the eye. Since most EOG-based applications need a large amount of data to be stored and transmitted, compression is required. A study on the use of discrete wavelet packet decomposition (DWPD) for compressing EOG signals is presented in this paper. The EOG signals are recorded using a commercial bioamplifier. The compression algorithm is implemented using the MATLAB software. The performance of the compression was evaluated using three parameters: compression ratio (CR), energy retention, and percent root-mean-square difference (PRD). The experimental results show that the DWPD allows for CRs of 82% while retaining almost 100% of the signal energy with a PRD of only 7%.

关键词： compression ratio electrooculogram percent root-mean-square difference energy recovery wavelet decomposition wavelet packet decomposition

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GABIC: GRAPH-BASED ATTENTION BLOCK FOR IMAGE compression 31

GABIC: GRAPH-BASED ATTENTION BLOCK FOR IMAGE COMPRESSION

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2024 International conference on Image Processing

作者： Spadaro, Gabriele Presta, Alberto Tartaglione, Enzo Giraldo, Jhony H. Grangetto, Marco Fiandrotti, Attilio Univ Turin Turin Italy T elecom Paris Inst Polytech Paris LTCI Paris France

ISBN: (纸本)9798350349405;9798350349399

While standardized codecs like JPEG and HEVC-intra represent the industry standard in image compression, neural Learned Image compression (LIC) codecs represent a promising alternative. In detail, integrating attention mechanisms from Vision Transformers into LIC models has shown improved compression efficiency. However, extra efficiency often comes at the cost of aggregating redundant features. This work proposes a Graph-based Attention Block for Image compression (GABIC), a method to reduce feature redundancy based on a k-Nearest Neighbors enhanced attention mechanism. Our experiments show that GABIC outperforms comparable methods, particularly at high bit rates, enhancing compression performance.

关键词： Learned Image compression Graph Neural Network Attention Mechanism Vision Transformer

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Region of Interest Loss for Anonymizing Learned Image compression 20

Region of Interest Loss for Anonymizing Learned Image Compre...

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ieee 20th International conference on Automation Science and Engineering (CASE)

作者： Liebender, Christoph Bezerral, Ranulfo Ohno, Kazunori Tadokoro, Satoshi Tohoku Univ Grad Sch Informat Sci Sendai Miyagi Japan Julius Maximilian Univ Wurzburg Robot Grp Wurzburg Germany

ISBN: (纸本)9798350358513;9798350358520

The use of AI in public spaces continually raises concerns about privacy and the protection of sensitive data. An example is the deployment of detection and recognition methods on humans, where images are provided by surveillance cameras. This results in the acquisition of great amounts of sensitive data, since the capture and transmission of images taken by such cameras happens unaltered, for them to be received by a server on the network. However, many applications do not explicitly require the identity of a given person in a scene;An anonymized representation containing information of the person's position while preserving the context of them in the scene suffices. We show how using a customized loss function on region of interests (ROI) can achieve sufficient anonymization such that human faces become unrecognizable while persons are kept detectable, by training an end-toend optimized autoencoder for learned image compression that utilizes the flexibility of the learned analysis and reconstruction transforms for the task of mutating parts of the compression result. This approach enables compression and anonymization in one step on the capture device, instead of transmitting sensitive, nonanonymized data over the network. Additionally, we evaluate how this anonymization impacts the average precision of pre-trained foundation models on detecting faces (MTCNN) and humans (YOLOv8) in comparison to non-ANN based methods, while considering compression rate and latency. The code and model weights for this approach, including usage examples are publicly available on GitHub: https://***/BRN-Hub/anon-compression

关键词： Anonymity

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APLASE: compression using Adaptive Piecewise Linear Approximation and Sparse Encoding

APLASE: Compression using Adaptive Piecewise Linear Approxim...

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2025 ieee International conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Saxena, Ravi Raj Prabhakar, T.V. Kuri, Joy Murthy, Chandra R. Dept. of Electronic System Engineering Indian Institute of Science Bengaluru India Dept. of Electrical Communication Engineering Indian Institute of Science Bengaluru India

ISBN: (纸本)9798350368741

This work focuses on compressing vast amounts of time series data from IoT sensors while achieving low reconstruction error, at a low compression ratio (ratio of output data size to input data size), for efficient storage and transmission. We investigate two lossy compression techniques: Adaptive Piecewise Linear Approximation (APLA) and Sparse Encoding (SE). APLA reduces data volume by leveraging the temporal correlation in time series data, while SE captures small, complex variations that APLA might miss. We identify a signal characteristic parameter, denoted by σd, which is used to derive error bounds for SE using σd, the dictionary used, and the input compression parameters. We also introduce APLASE, a novel compression technique that combines the two techniques by applying SE to the reconstruction error generated by APLA. We evaluate APLA, SE and APLASE using data from the NASA flight data recorder database consisting of recordings from 116 sensors. Our results show that APLASE consistently outperforms both APLA and SE individually, as well as the recent QoZ algorithm, particularly at lower compression ratios. © 2025 ieee.

关键词： compression Piecewise Linear Approximation Sparse Encoding

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CDCache: Space-Efficient Flash Caching via compression-before-Deduplication

CDCache: Space-Efficient Flash Caching via Compression-befor...

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ieee conference on Computer Communications (ieee INFOCOM)

作者： Xiao, Hengying Li, Jingwei Ren, Yanjing Wang, Ruijin Zhang, Xiaosong Univ Elect Sci & Technol China Chengdu Peoples R China Chinese Univ Hong Kong Hong Kong Peoples R China

ISBN: (纸本)9798350383515;9798350383508

Large-scale storage systems boost I/O performance via flash caching, but the underlying storage medium of flash caching incurs significant financial costs and also exhibits low endurance. Previous studies adopt compression-after-deduplication to mitigate writing redundant contents into the flash cache, so as to address the cost and endurance issues. However, deduplication and compression have conflicting preferable cases, and compression-after-deduplication essentially compromises the space-saving benefits of either deduplication or compression. To simultaneously preserve the benefits of both approaches, we explore compression-before-deduplication, which applies compression to eliminate byte-level redundancies across data blocks, followed by deduplication to write only a single copy of duplicate compressed blocks into the flash cache. We present CDCACHE, a space-efficient flash caching system that realizes compression-before-deduplication. It proposes to dynamically adjust the compression range of data blocks, so as to preserve the effectiveness of deduplication on the compressed blocks. Also, it builds on various design techniques to approximately estimate duplicate data blocks and efficiently manage compressed blocks. Trace-driven experiments show that CDCACHE improves the read hit ratio and the write reduction ratio of a previous compression-after-deduplication approach by up to 1.3x and 1.6x, respectively, while it only has small memory overhead for index management.

关键词： Information management

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A Non-reference Just Recognized Distortion Prediction Framework for Object Detection Task

A Non-reference Just Recognized Distortion Prediction Framew...

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data compression conference (DCC)

作者： Liu, Yichen Yin, Haibing Wang, Hongkui Wang, Xia Yin, Lida Hangzhou Dianzi Univ 11582 St Hangzhou 310018 Peoples R China Hangzhou Dianzi Univ Lishui Res Inst Lishui 323000 Peoples R China Lishui Middle Sch Lishui 323000 Peoples R China

ISBN: (纸本)9798350385885;9798350385878

This work proposed a non-reference Just Recognized Distortion (JRD) prediction framework for object detection task based on Generative Adversarial Network (GAN) image generation. Inspired by the concept of Just Noticeable Difference (JND), the JRD is used to describe the threshold of image distortion acceptable for machine vision tasks. Centered around JRD, the proposed framework primarily consisted of a JRD image generation network and a residual-guided JRD regression network. Among them, the JRD image generation network was trained in conjunction with a multi-scale discriminator in the form of GAN. Additionally, we had compiled a dataset comprising over 130,000 images for the YOLOv7 object detection task, and it was used to validate the effectiveness of our proposed framework. Experiments indicated that the framework can approximate the JRD for object detection task with notable accuracy. Consequently, adopting our proposed framework for image compression could reduce the bitrate by 50% while maintaining high accuracy in task. © 2024 ieee.

关键词： Generative Adversarial Network Image compression Just Recognized Distortion Machine Vision Object Detection

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C3: High-performance and low-complexity neural compression from a single image or video

C3: High-performance and low-complexity neural compression f...

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

作者： Kim, Hyunjik Bauer, Matthias Theis, Lucas Schwarz, Jonathan Richard Dupont, Emilien Google DeepMind London England Harvard Univ Cambridge MA 02138 USA

ISBN: (纸本)9798350353006

Most neural compression models are trained on large datasets of images or videos in order to generalize to unseen data. Such generalization typically requires large and expressive architectures with a high decoding complexity. Here we introduce C3, a neural compression method with strong rate-distortion (RD) performance that instead overfits a small model to each image or video separately. The resulting decoding complexity of C3 can be an order of magnitude lower than neural baselines with similar RD performance. C3 builds on Cool-chic [43] and makes several simple and effective improvements for images. We further develop new methodology to apply C3 to videos. On the CLIC2020 image benchmark, we match the RD performance of VTM, the reference implementation of the H.266 codec, with less than 3k MACs/pixel for decoding. On the UVG video benchmark, we match the RD performance of the Video compression Transformer [60], a well-established neural video codec, with less than 5k MACs/pixel for decoding.

关键词： image compression Neural compression neural fields overfitted codecs video compression

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Compressed Input data Format of Quantum Annealing Emulator

Compressed Input Data Format of Quantum Annealing Emulator

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data compression conference (DCC)

作者： Shimomai, Sohei Ueda, Kei Kimura, Shinji Waseda Univ Tokyo Japan Fujitsu Tokyo Japan

ISBN: (纸本)9798350347951

Recently, Quantum Annealing (QA) has attracted attention as an efficient algorithm for combinatorial optimization problems. In QA, the input data size becomes large and its reduction is important for the hardware emulation since its usable memory size and its bandwidth are limited. As an improved Coordinate (COO) format for a sparse matrix, Compressed Sparse Row (CSR) has been proposed [1]. We have checked CSR on an Ising model but found that the compression efficiency is not high. © 2023 ieee.

关键词： Ising Model Quantum Annealing Sparse Matrix

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Efficient Side-Channel data compression Using Autoencoder Networks for Enhanced Leakage Analysis 7

Efficient Side-Channel Data Compression Using Autoencoder Ne...

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7th International conference on Automation Electronics and Electrical Engineering

作者： Yan, Mingkai Zhang, Lixiong Li, Yujia Wu, Hanbing Li, Yuran CATARC Software Testing Tianjin Co Ltd Tianjin Peoples R China China Automot Technol & Res Ctr Co Ltd Tianjin Peoples R China

ISBN: (纸本)9798350377040;9798350377033

Side-channel data compression techniques are designed to reduce the dimensionality of input data while preserving critical information, thereby improving the efficiency of side-channel analysis. Traditional methods of side-channel analysis can be broadly classified into two categories: peak extraction techniques and data integration techniques, based on the scale and principles of the input data. In this study, we present a novel side-channel data compression technique utilizing convolutional autoencoder networks. We apply this method to power-side channel data collected from an SM4 encryption circuit implemented on a secure chip. Experimental results show that our proposed technique surpasses conventional methods in Test Vector Leakage Assessment (TVLA) analysis and achieves a 35% improvement in efficiency in Measurements to Disclosure (MTD) analysis, while maintaining the same compression rate. These results underscore the effectiveness and potential of our approach in enhancing side-channel analysis.

关键词： Side Channel Analysis data compression Autoencoder Security Chip

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Sketching and Streaming for Dictionary compression

Sketching and Streaming for Dictionary Compression

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data compression conference (DCC)

作者： Becker, Ruben Canton, Matteo Cenzato, Davide Kim, Sung-Hwan Kodric, Bojana Prezza, Nicola Ca Foscari Univ Venice Via Torino 155 I-30172 Venice Italy Univ Udine Via Scienze 206 I-33100 Udine Italy

ISBN: (纸本)9798350385885;9798350385878

We initiate the study of sub-linear sketching and streaming techniques for estimating the output size of common dictionary compressors such as Lempel-Ziv '77, the run-length Burrows-Wheeler transform, and grammar compression. To this end, we focus on a measure that has recently gained much attention in the information-theoretic community and which approximates up to a polylogarithmic multiplicative factor the output sizes of those compressors: the normalized substring complexity function delta. As a matter of fact, delta itself is a very accurate measure of compressibility: it is monotone under concatenation, invariant under reversals and alphabet permutations, sub-additive, and asymptotically tight (in terms of worst-case entropy) for representing strings, up to polylogarithmic factors. We present a data sketch of O(epsilon(-3) log n + epsilon(-1) log(2) n) words that allows computing a multiplicative (1 +/- epsilon)-approximation of delta with high probability, where n is the string length. The sketches of two strings S-1, S-2 can be merged in O(epsilon(-1) log(2) n) time to yield the sketch of {S-1, S-2}, speeding up the computation of Normalized compression Distances (NCD). If random access is available on the input, our sketch can be updated in O(epsilon(-1) log(2) n) time for each character right-extension of the string. This yields a polylogarithmic-space algorithm for approximating delta, improving exponentially over the working space of the state-of-the-art algorithms running in nearly-linear time. Motivated by the fact that random access is not always available on the input data, we then present a streaming algorithm computing our sketch in O(pn center dot log n) working space and O(epsilon(-1) log(2) n) worst-case delay per character. We show that an implementation of our streaming algorithm can estimate delta on a dataset of 189GB with a throughput of 203MB per minute while using only 5MB of RAM, and that our sketch speeds up the computation of all-pairs

关键词： data compression dictionary compression repetitive sequences sketching streaming sublinear space algorithms substring complexity

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