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

共 16713 条记录，以下是371-380 订阅

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Lossless Image compression using K-Means Clustering in Color Pixel Domain 5

Lossless Image Compression using K-Means Clustering in Color...

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5th ieee International conference on Computing, Power, and Communication Technologies, IC2PCT 2024

作者： Kumari, Rimjhim Sriramulu, Srinivasan Galgotias University Department of Computer Science and Engineering Greater Noida India Galgotias University School of Computing Science and Engineering Greater Noida India

ISBN: (纸本)9798350383522

Compressing images is a method for shrinking the image's dimensions using a particular algorithm. Image compression is a solution associated with transmitting and storing large amounts of data for digital images. While image storage is necessary for medical images, satellite images, documents, and pictures, image transmission includes a variety of applications like television broadcasting, remote sensing via satellite, and other long-distance communication. These kinds of applications deal with image compression. Lossy compression and lossless compression are two separate methods for compressing images. While unneeded metadata is deleted in lossless compression to minimize file size, lossy compression permanently removes part of the original data to reduce file size. The terms 'supervised learning' and 'unsupervised learning' refer to two different machine learning techniques. As opposed to a non-supervised learning algorithm, which uses unlabeled input data, supervised learning uses labeled data. While clustering is a subtype of unsupervised learning, classification, and regression are additional subcategories of supervised learning. This paper discusses the application of clustering using K-means. This methodology explores the concept of lossless picture compression is the process of reducing the size of an image without sacrificing its quality which is achieved using k-mean clustering. The proposed methodology achieves higher compression ratios than other existing approaches to image compression algorithms. © 2024 ieee.

关键词： clustering compression image k-mean lossless unsupervised

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Modified Arithmetic Coding to Increase the compression Rate of Text data 2

Modified Arithmetic Coding to Increase the Compression Rate ...

引用

2nd ieee International conference on Contemporary Computing and Communications, InC4 2024

作者： Venkatesh, Veeramuthu Reddy, Kalluru Amarnath Anushiadevi, R. Vinay, Nurukurthi Poorna Chandra Sai Raj, Madam Sushmitha School of Computing SASTRA Deemed University Tamilnadu Thanjavur India

ISBN: (纸本)9798350383652

In the past two decades, hardware and software have advanced quickly due to technological advancement. Global data distribution and storage have become simpler as a result. However, the bandwidth cannot handle the volume of information being created. As a result, researchers are now looking into data compression. Numerous data compression techniques have been developed, including Shannon Fano, Huffman, Lempel Ziv, and Arithmetic coding techniques. Characters are typically expressed in ASCII code using a predetermined number of bits per character. Lossless data compression uses an entropy coding method called Arithmetic Coding (AC). In the arithmetic encoding of a string similar to Huffman coding, symbols seen frequently are encoded with more bits than symbols seen less frequently. Unlike other entropy encoding techniques like Huffman coding, a single arbitrary-precision fraction between 0 and 1 encodes the entire message in arithmetic coding. In this proposed method, the arbitrary-precision fraction is converted into its equivalent binary form, split into groups of 8 bits, and converted into its identical UTF-8-character set, whose length is far lower than the arbitrary-precision fraction. Huffman code is used to compress further the UTF-8 character set. © 2024 ieee.

关键词： Encoding (symbols)

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Partitioned Inverted Index compression Using Hierarchical Dirichlet Process 4

Partitioned Inverted Index Compression Using Hierarchical Di...

引用

4th International conference on Neural Networks, Information and Communication Engineering, NNICE 2024

作者： He, Runzhu Qu, Youli Beijing Jiaotong University Ministry of Education School of Computer and Information Technology Dept. Key Laboratory of Big Data & Artifcial Intelligence in Transportation Beijing China

ISBN: (纸本)9798350394375

In large-scale search engines, the core data structure is the inverted index, essentially a collection of integer sequences within inverted lists. Precise partitioning of these sequences enables efficient query processing. The concept of partitioning by quantity and universe has been established for years in the field of inverted index compression algorithms. The volume of data within a post-partitioned area, defined as density, is a key determinant of the compression efficiency of partitioned inverted index algorithms. Previous studies have focused on creating denser partitioned areas atop existing inverted file indexes to improve compression efficiency. This paper proposes a novel approach that utilizes the Hierarchical Dirichlet Process model to extract document themes, categorizes documents based on these themes, and then constructs file indexes accordingly to create denser partitioned areas. Compared with other inverted index compression methods, this algorithm achieves a favorable balance between time and space efficiency. © 2024 ieee.

关键词： Search engines

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Efficient data Confidence Fabrics With Compact Annotations 32

Efficient Data Confidence Fabrics With Compact Annotations

引用

32nd International conference on Network Protocols

作者： Khalid, Asfa Murphy, Sean Og Sreenan, Cormac Roedig, Utz Univ Coll Cork Sch Comp Sci & Informat Technol CSIT Cork Ireland

ISBN: (纸本)9798350351712;9798350351729

The need to trust data has become a key requirement in modern distributed systems. To facilitate measurable trust and confidence in data and applications spanning heterogeneous systems, the emerging concept of a data Confidence Fabric (DCF) offers a compelling solution. data producers and processors provide metadata, known as annotations, recording trust insertion along the data delivery chain. Thus, it is possible to asses the trustworthiness of data before processing it. While a DCF, such as the Alvarium framework, enables this management of trust, there is a cost in terms of the overheads associated with security annotations themselves. To improve efficiency of a DCF, we therefore propose a set of techniques for making annotations more compact and to reduce the number of DCF transactions. Our work shows that transaction efficiency gains of up to 93% in our considered use cases can be achieved.

关键词： data compression data Annotation Trust Zero Trust data Confidence Fabric Security

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RESNERF-PCAC: SUPER RESOLVING RESIDUAL LEARNING NERF FOR HIGH EFFICIENCY POINT CLOUD ATTRIBUTES CODING 31

RESNERF-PCAC: SUPER RESOLVING RESIDUAL LEARNING NERF FOR HIG...

引用

2024 International conference on Image Processing

作者： Umair, Sajid Kathariya, Birendra Li, Zhu Akhtar, Anique Van der Auwera, Geert Univ Missouri Kansas City MO 64110 USA Qualcomm Technol Inc San Diego CA USA

ISBN: (纸本)9798350349405;9798350349399

A point cloud (PC) is a popular 3D data representation that poses challenges due to its size, dimensionality, and unstructured nature. This paper introduces the Residual Neural Radiance Field for Point Cloud Attribute Coding (ResNeRF-PCAC), a novel approach for point cloud attribute compression. ResNeRF-PCAC combines sparse convolutions with neural radiance fields, to create a highly efficient attribute coding solution. It initially downscales the point cloud to generate a coarse thumbnail point cloud and encodes it using the G-PCC attribute encoder. The thumbnail PC is upsampled using a super-resolution network to generate a recolored PC. Color attribute residuals are then computed between the original and the super-resolved recolored PC. A ResNeRF network is employed to predict these residuals. The trained ResNeRF weights are compressed into a bitstream. The thumbnail bitstream and the compressed model weights are then transmitted to the decoder. Sparse convolution-based super-resolving network weights are shared and common across all content and need not to be signaled. Experiments on the MPEG-8i dataset demonstrate superior performance in terms of reconstruction quality and compression ratio compared to G-PCC-RAHT and G-PCC-Predlift for both v14 and v21.

关键词： 3D Point Cloud Attributes compression Deep Learning NeRF Model compression

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Huffman Tree compression and Lempel-Ziv Coding Using Java

Huffman Tree Compression and Lempel-Ziv Coding Using Java

引用

2024 International conference on Decision Aid Sciences and Applications, DASA 2024

作者： Nuha, Hilal H. Rizky Putra Sailellah, Hassan Abo Absa, Ahmed School of Computing Telkom University Bandung Indonesia University of Palestine Software Engineering Gaza Strip Palestine

ISBN: (纸本)9798350369106

Lossless data compression is necessary to reduce transmission costs while maintaining data integrity. This paper describes Kompressor as a Huffman and Lempel Ziv (LZ) compression algorithm project using Java. All major Java class implementations are discussed in detail. The algorithm is evaluated for three different files to measure performance such as compression ratio, code rate, and efficiency. The program is shown to be capable of performing compression and decompression as proposed in the original algorithm for different parameter values. Huffman encoding, in particular, is capable of shrinking files to less than 70% of their original file size. It is hoped that this project will be useful for academic and scientific purposes, especially for courses based on data compression. © 2024 ieee.

关键词： Java programming language

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Wavelet based PMU data compression for Wide Area Measurement System

Wavelet based PMU Data Compression for Wide Area Measurement...

引用

2024 OPJU International Technology conference on Smart Computing for Innovation and Advancement in Industry 4.0, OTCON 2024

作者： Zuhaib, Mohd Gla University Electrical Engineering Department Mathura India

ISBN: (纸本)9798350373783

Phasor Measurement Units generates massive data volumes, making its data compression essential in modern power systems. PMUs collect high-frequency voltage and current phasor readings from across the grid in real time for grid monitoring and control. data can flood communication networks and exhaust storage resources without compression, reducing system efficiency and operability. Power utilities can optimize bandwidth utilization by compressing PMU data, ensuring timely data transfer for crucial real-time processes while reducing storage costs and network congestion. PMU data compression is vital for efficient and effective power system management since compressed data is useful for post-event analysis, grid optimization, and regulatory compliance. This paper examines the efficacy of wavelets in compressing large volumes of PMU data without compromising the information contained within it. © 2024 ieee.

关键词： Phasor measurement units

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An Index-free Coding compression Mechanism for Vision Transformers

An Index-free Coding Compression Mechanism for Vision Transf...

引用

2024 ieee International conference on Integrated Circuits, Technologies and Applications, ICTA 2024

作者： Yan, Han Zou, Zihan Chen, Shikuang Zhang, Chen Yang, Guang Cai, Hao Liu, Bo Southeast University School of Integrated Circuits Nanjing210096 China

ISBN: (纸本)9798331530709

Vision Transformers (ViTs) have emerged as powerful models in computer vision but their large parameter counts and substantial memory access costs pose challenges for deployment on resource-constrained platforms. This paper proposes an index-free coding compression mechanism for ViTs, designed to enhance compression efficiency and reduce power consumption significantly. Our mechanism incorporates a heuristic clustering-based coding strategy, a dual-aspect coding evaluation method, and a hardware-friendly index-free decoder design. These innovations provide orthogonal improvements to existing compression methods, further enhancing data compression efficiency. Validated through post-layout simulation and synthesis under the TSMC 28nm process, our method achieves a 2 × compression ratio and enhances energy efficiency by 1.99 × compared to existing methods. © 2024 ieee.

关键词： Codes (symbols)

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Assessing Air-Interface dataset Similarity and Diversity for AI-Enabled Wireless Communications 59

Assessing Air-Interface Dataset Similarity and Diversity for...

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59th Annual ieee International conference on Communications (ieee ICC)

作者： Tang, Hanning Yang, Liusha Zhou, Rui Liang, Jing Wei, Hong Wang, Xuan Shi, Qingjiang Luo, Zhi-Quan Chinese Univ Hong Kong Shenzhen Res Inst Big Data Shenzhen Peoples R China Huawei Co Wireless Res Dept Shanghai Peoples R China Tongji Univ Sch Software Engn Shanghai Peoples R China

ISBN: (纸本)9798350304060;9798350304053

The integration of artificial intelligence (AI) into wireless communication systems is set to profoundly transform the design and optimization of emerging sixth-generation (6G) networks. The success of AI-driven wireless systems hinges on the quality of the air-interface data, which is fundamental to the performance of AI algorithms. Within data quality assessment (DQA), the measurement of similarity and diversity stands as crucial. Similarity assesses the consistency of datasets in mirroring their intrinsic statistical properties, which is essential for AI model accuracy. In contrast, diversity relates to the models' ability to generalize across various contexts. This paper concentrates on these aspects of DQA and proposes a comprehensive framework for analyzing similarity and diversity in wireless air-interface data. Catering to various data types, such as channel state information (CSI), signal-to-interference-plus-noise ratio (SINR), and reference signal received power (RSRP), the framework is validated using CSI data. Through this validation, we demonstrate the framework's efficacy in improving CSI compression and recovery in Massive Multiple-Input Multiple-Output (MIMO) systems, highlighting its significance and versatility in complex wireless network environments.

关键词： data quality assessment AI-enabled wireless communication similarity diversity

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PATCH INHERENT FEATURE GUIDED MASK SELECTION FOR IMAGE compression 49

PATCH INHERENT FEATURE GUIDED MASK SELECTION FOR IMAGE COMPR...

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49th ieee International conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Huang, Xianhan Zhu, Qingmeng Yu, Zhipeng Liu, Kun He, Hao Chinese Acad Sci Inst Software Beijing Peoples R China Beijing Univ Technol Beijing Peoples R China

ISBN: (纸本)9798350344868;9798350344851

Recent advances in Learned Image compression (LIC) utilize the powerful Masked Image Modeling (MIM) framework (e.g., MAE). However, the random masking treats all patches equally, which could lead (i) missing critical information and (ii) poor robustness, since different patches may have different contributions to image reconstruction. The principle experiment shows that the patch-level inherent feature evaluation is highly correlated with the quality of the reconstructed image. Based on this key insight, we propose a simple mask selection approach based on the patch's inherent feature. Specifically, we design plug-and-play frequency and spatial inherent feature modules to select masks, enhancing various MIMs based on the MAE model and achieving high-quality image reconstruction and strong robustness at high mask rates for data-intensive compression tasks.

关键词： Image compression Masked Image Modeling Masked Autoencoder

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