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

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Simple Worst-Case Optimal Adaptive Prefix-Free Coding

Simple Worst-Case Optimal Adaptive Prefix-Free Coding

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data compression conference (DCC)

作者： Gagie, Travis Dalhousie Univ Halifax NS Canada

ISBN: (纸本)9781665478939

Suppose we want to store a string $S[1..n]$ over an alphabet of size $\\sigma$ using adaptive prefix-free coding with fast encoding and decoding. If we are not too concerned about compression, we can process $/\\mathrm{S}$ in blocks of $\\sigma$ characters as follows: we encode or decode the first block with a Shannon code for the uniform distribution; to encode or decode the $ith$ block, for $i > 1$, we build a Shannon code for the average of the distribution of characters we have seen so far and of the uniform distribution; since this averaged distribution assigns each character probability at least $1/(2\\sigma)$ (even when we have not seen it), the code-tree has height at most $[\\text{lg}(2\\sigma)] < \\text{lg}\\sigma+2$ and can be represented with an $O(\\sigma)$ -space table that takes $O(\\sigma)$ time to build and allows constant-time encoding and decoding of characters - so we process the block in constant time per character. We store the first $\\sigma$ copies of each distinct character using fewer than $\\text{lg}\\sigma+2$ bits each and, for $j > \\sigma$, we store the $j\\text{th}$ copy $S[k]$ using at most $\\left[\\text{lg}(\\frac{1}{2}(\\frac{j-\\sigma+1}{\\sigma\\lfloor(k-1)/\\sigma\\rfloor}+\\frac{1}{\\sigma}))^{-1}\\right]< \\text{lg}\\frac{k}{j-\\sigma+1}+2$ bits. Fairly straight forward calculation shows that when $\\sigma\\ll n$, we store at most $n(H+2)+o(n)$ bits overall, where $H$ is the entropy of the distribution of characters in $S$.

关键词： Codes data compression Entropy Decoding Channel coding

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A Novel Lossless Image compression Algorithm for Optimized Memory Storage

A Novel Lossless Image Compression Algorithm for Optimized M...

引用

2023 International conference on Computational Intelligence and Sustainable Engineering Solution, CISES 2023

作者： Jindal, Gaurav Bhatia, Gaurav Kumar, Virendra Glbitm Gr. Noida India

ISBN: (纸本)9798350323917

data compression is a specific and special task that goes on and on as more and more data is required to be stored in the memory. For memory usage, it is always 'smaller is better'. But, of course, there is always a threshold limit which may never be broken. Saving a graphical file is always challenging task as whenever we try to compress it, there is a possibility of loss of data whether in terms of colour or in terms of sharpness. The study (algorithm) presented in this paper is a methodical formation where the size of a graphical file has been cut to half without any type of loss of information. Neither the sharpness nor the colour gets affected. Through the paper, a novel algorithm has been developed which compresses an image file to half its previous size. The best part is that the complexity of algorithm is comparatively low for compressing as well as de-compressing (re-generation) purpose. © 2023 ieee.

关键词： Image compression

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FedSaw: Communication-Efficient Cross-Silo Federated Learning with Adaptive compression 20

FedSaw: Communication-Efficient Cross-Silo Federated Learnin...

引用

20th ieee International conference on Mobile Ad Hoc and Smart Systems, MASS 2023

作者： Ying, Chen Li, Baochun Li, Bo University of Toronto Department of Electrical and Computer Engineering Canada Hong Kong University of Science and Technology Department of Computer Science and Engineering Hong Kong

ISBN: (纸本)9798350324334

Cross-silo federated learning (FL) is an emerging approach for institutions to collaboratively train a machine learning model without sharing their siloed data. However, it conventionally requires institutions to centrally store their clients' data, posing a threat to clients' privacy. This paper thus studies a preferable setting to leave data distributed to clients, where traditional FL is used not only across institutions but also among their clients. As this new setting inherits or even exacerbates the major problem of communication inefficiency in the traditional setting, we explore the feasibility of leveraging two compression techniques, pruning and quantization, to improve communication efficiency. Starting by applying off-the-shelf pruning and quantization mechanisms, we observe that they could largely reduce communication overhead with a negligible reduction, sometimes even a slight increase, in training performance. By mathematically analyzing the impact of compression on the performance of the trained model, we find that pruning and quantizing with a proper amount can offset possible performance degradation due to non-i.i.d data. Based on this finding, we propose FedSaw, a new cross-silo FL framework that can improve communication efficiency by adaptively tuning the pruning amount and quantizing updates throughout training. In our extensive evaluation with six benchmark datasets, FedSaw consistently outperformed its state-of-the-art competitors. It decreased the wall-clock training time and communication overhead used for converging to the target accuracy by up to 86.7% and 91.5%, respectively. © 2023 ieee.

关键词： Efficiency

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Efficient PCA-based image compression via secure outsourcing edge cloud 24

Efficient PCA-based image compression via secure outsourcing...

引用

24th ieee International conference on High Performance Computing and Communications, 8th ieee International conference on data Science and Systems, 20th ieee International conference on Smart City and 8th ieee International conference on Dependability in Sensor, Cloud and Big data Systems and Application, HPCC/DSS/SmartCity/DependSys 2022

作者： Luo, Yuling Zhang, Shiqi Zhang, Shunsheng Liu, Junxiu Liang, Ce Yang, Su Guangxi Normal University Guangxi Key Lab of Brain-inspired Computing and Intelligent Chips Guilin China Swansea University Department of Computer Science Swansea United Kingdom

ISBN: (纸本)9798350319934

The increasing of image pixel size in recent years has lifted the expense for the image data storage and transmission drastically. Image compression could be one effectively solution to alleviate this difficulty. However, in some cases, the computational terminal resource is constrained and insufficient to effectively perform the image compression. An image compression based on principal component analysis (PCA) outsourcing protocol is proposed in this paper to handle such problems. The abundant computing resources of the edge cloud are used to perform the image compression effectively that the terminal cannot. The image is encrypted and sent to the cloud by terminal. The encrypted matrix is directly calculated by the cloud and the computational result is returned to the terminal. After the result is received by the terminal, the correctness of the result is verified. The result will be decrypted if it passed the verification. Otherwise, the result is returned to the edge cloud for recalculation. According to a series of comprehensive performance analysis, the proposed protocol can substantially improve the efficiency of local computation, also guarantee the privacy of the terminal and correctness of the results. © 2022 ieee.

关键词： Image compression

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A DSDV Routing Structure for Efficient compression 8

A DSDV Routing Structure for Efficient Compression

引用

8th International conference on Computer and Communication Systems, ICCCS 2023

作者： Zhang, Yalei Hou, Shujuan Zhang, Qin Beijing Institute of Technology School of Information and Electronics Beijing China

ISBN: (纸本)9781665456128

The traditional DSDV protocol is not suitable for large-scale Ad hoc networks due to high overhead. And the size of periodic packets increases linearly with the number of nodes in the network. Oversized packet will cause the MTU-limited MAC layer to generate more fragments and degrade network performance particularly in the narrow-band network. In this paper, we analyze the structure of the DSDV packet and propose a column-based hybrid encoding structure for DSDV, called CHEDSDV. To better contextualize the packet content, we switch from storing data by entry to storing by field. We also construct an out-of-band dictionary for the ADDR field, analyze the distribution characteristics and relationships of other fields, and assess the suitability of general compression algorithms for them. Finally, we tested the hybrid encoding method based on NS3 simulation data. The results show that the hybrid encoding structure is more suitable for compression and achieves a better compression ratio than direct packet compression. © 2023 ieee.

关键词： Ad hoc networks

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R-PCC: A Baseline for Range Image-based Point Cloud compression 39

R-PCC: A Baseline for Range Image-based Point Cloud Compress...

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ieee International conference on Robotics and Automation (ICRA)

作者： Wang, Sukai Jiao, Jianhao Cai, Peide Wang, Lujia Hong Kong Univ Sci & Technol Dept Comp Sci & Engn Hong Kong 999077 Peoples R China Hong Kong Univ Sci & Technol Dept Elect & Comp Engn Hong Kong 999077 Peoples R China

ISBN: (纸本)9781728196817

In autonomous vehicles or robots, point clouds from LiDAR can provide accurate depth information of objects compared with 2D images, but they also suffer a large volume of data, which is inconvenient for data storage or transmission. In this paper, we propose a Range image-based Point Cloud compression method, R-PCC, which can reconstruct the point cloud with uniform or non-uniform accuracy loss. We segment the original large-scale point cloud into small and compact regions for spatial redundancy and salient region classification. Our range image-based method can keep and align all points from the original point cloud in the reconstructed point cloud, and the setting of the quantization module restricts the maximum reconstruction error. In the experiments, we prove that our easier FPS-based segmentation method can achieve better performance than instance-based segmentation methods such as DBSCAN, and our non-uniform compression framework shows a great improvement on the downstream tasks compared with the state-of-the-art large-scale point cloud compression methods. Our real-time method can achieve 40x compression ratio without affecting downstream tasks, to act as a baseline for range image-based point cloud compression. The code is available on https://***/StevenWang30/***.

关键词： Point cloud compression Image segmentation Image coding Three-dimensional displays Simultaneous localization and mapping Redundancy Real-time systems

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Investigating Applications of Run Length Encoding in data compression & Source Coding 15

Investigating Applications of Run Length Encoding in Data Co...

引用

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

作者： Rongali, Aruna Sri Dhabliya, Dharmesh Pandey, Ishaan Sachdeva, Ankit Praveena, Hirald Dwaraka Aich, Shibani C. University of The Cumberlands Department Information Technology United States Vishwakarma Institute of Technology Department of Information Technology Maharashtra Pune India Bml Munjal University Computer Science Haryana Gurgaon India Chitkara University Centre of Research Impact and Outcome Punjab Rajpura140417 India Mohan Babu University Department of Ece Andhra Pradesh Tirupati517 102 India Amrita Vishwa Vidyapeetham Department of English Amritapuri India

ISBN: (纸本)9798350370249

Run period Encoding (RLE) is a powerful and efficient approach for fact compression and supply coding. It is generally applied to the lossless compression of grayscale or bi-tonal (black-and-white) photos and digital pix. Significantly, RLE decreases the statistics required to save and transmit virtual facts. That is completed by making the information greater compact and casting off redundancy. Typically, RLE counts the number of consecutive occurrences of a given image or sign and then encodes those occurrences into an unmarried cost. By encoding multiple repetitions of a symbol in this manner, RLE can reduce the entire quantity of statistics wanted for garage or transmission. The RLE set of rules works by looking for long strings of characters that may be compressed into fewer characters. Exploring the potential packages of RLE in supply coding and records compression is an ongoing study subject matter within the area. © 2024 ieee.

关键词： compression Decrease Encoding Quantity Transmission Unmarried

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Electricity data analysis method based on K-means clustering algorithm 6

Electricity data analysis method based on K-means clustering...

引用

6th ieee Information Technology and Mechatronics Engineering conference, ITOEC 2022

作者： Lei, Qinyi Hu, Cong Hong, Dehua Liu, Cuiling Zhao, Linyan Sun, Qi Information and Communication Branch of State Grid Anhui Electric Power Co. Ltd. Hefei China

ISBN: (纸本)9781665431859

With the rapid increase in the amount of grid data, more and more data needs to be backed up and restored in the data backup system. However, the similarity between each backup file exceeds 60%, and all storage on the hard disk is a waste of storage space, so it is proposed A DELTA compression method based on K-medoids clustering is used to remove duplicate data in backup data. By performing pairwise DELTA compression on the file blocks, the size of each compressed file is obtained as the similarity between the two file blocks. Through the obtained similarity, K-medoids clustering is performed as a preprocessing step before DELTA compression. Then, according to the clustering results of K-medoids, DELTA compression is performed after merging small file blocks. The test results show that this method can improve the compression rate and reduce the number of fingerprint searches in DELTA compression. © 2022 ieee.

关键词： data compression

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Electrocardiogram data compression Using Wavelet Transform and Variable-Length Run-Length Encoding 5

Electrocardiogram Data Compression Using Wavelet Transform a...

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5th International conference on Applied Machine Learning, ICAML 2023

作者： Cao, Xiang Yi, Yougen School of Physics and Electronics Central South University Changsha410083 China

ISBN: (纸本)9798350341416

data compression algorithm is an effective tool to reduce the storage requirements and improve the transmission efficiency in wearable ECG monitoring system. Therefore, an efficient ECG data compression scheme which based on discrete wavelet transform (DWT) and variable-length run-length encoding was presented in this paper. DWT is utilized to compute the transform coefficients of the original data. Then using the dead-zone quantization method to quantize wavelet coefficients, and the threshold is determined according to the energy packing efficiency (EPE). Coefficients below the threshold are removed directly, while other wavelet coefficients are kept at the preset quantization level. Further, using the variable-length run-length encoding algorithm to encode the quantized coefficients. The performance of the compression method was evaluated by compressing the ECG records from the MIT-BIH arrhythmia database. The average values of compression ratio (CR), quality score (QS), quality score 1 (QS1), quality score normalized (QSN), percent root mean square difference (PRD), percent root mean square difference 1 (PRD1), percent root mean square difference normalized (PRDN) and signal to noise ratio (SNR) of the proposed method are 20.48, 47.13, 4.85, 3.12, 0.47, 4.16, 6.86 and 24.12 dB respectively. Compared with other ECG compression schemes, the results show that this scheme has better performance. © 2023 ieee.

关键词： Electrocardiograms

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A Deep Learning Model for Precipitation Nowcasting Using data Fusion

A Deep Learning Model for Precipitation Nowcasting Using Dat...

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2024 ieee International conference on Multisensor Fusion and Integration for Intelligent Systems, MFI 2024

作者： Da Cruz, Ana Luisa Souza Castanheira Outeiro, Sidney Kopp, Luis Filipe De Farias, Claudio Miceli Rio de Janeiro Brazil

ISBN: (纸本)9798350368031

Climate change is intensifying precipitation events, leading to increased flooding and landslides, particularly in urban areas. Traditional Numerical Weather Prediction (NWP) models are essential for forecasting these events, but require extensive computational resources for high spatio-temporal resolution. Machine Learning (ML) approaches offer a more computationally efficient alternative, with fast prediction phases suitable for less powerful resources, while intensive training can be done asynchronously. We propose a deep learning architecture that incorporates an information data fusion module prior to a CNN-based decision module to reduce the input size while maintaining prediction accuracy. By testing the application of two different subspace methods, Principal Component Analysis decomposition (PCA) and Maximum pixel map, we reduce the number of input channels needed for the decision module. Experiments on the rainfall dataset compare the performance of the decision module with and without the fusion module. The experiments conclude that while the baseline original SmaAt-UNet remains the most reliable, the Fusion Module Max Pixel Map provides a substantial input compression while maintaining performance. This could enable the execution of forecasts on devices with reduced processing capabilities, such as mobile devices. © 2024 ieee.

关键词： data fusion

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