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检索条件"主题词=Compression Algorithms"

共 2082 条记录，以下是101-110 订阅

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Measuring Repetitive Sequences: A Lempel-Ziv compression-based Approach with Transition-Based Tokenization in Music Analysis

Measuring Repetitive Sequences: A Lempel-Ziv Compression-bas...

引用

IEEE MIT Undergraduate Research Technology Conference (URTC)

作者： Anton Chen Ross Greer Sidwell Friends School University of California Merced

ISBN: (数字)9798331531003

ISBN: (纸本)9798331531010

Repetition is a key element of music, providing structure, enhancing melodies, and fostering familiarity. However, its objective quantification toward advances in music information retrieval and generative AI remains challenging. Because most compression algorithms exploit repetition in a sequence, the more compactly a sequence can be compressed, the more repetition is likely to be present, and Lempel-Ziv compression has been a useful technique toward this analysis. However, the method of tokenizing symbolic music for this analysis can affect the output; often, repetition in music takes place when a sequence is transposed to an alternate key. Though the notes may be different in absolute position, this is still a musically significant repetition, yet it would be regarded as “new” by a note-based tokenization scheme. In this research, we present a new transition-based tokenization scheme, and show that this tokenization method, when paired with compressibility analysis, can better reflect repetitive structure. We apply this analysis to 12 musical compositions, assessing their compressibility using both the note-based and transition-based tokenizations, finding t hat compositions with prominent transposition repetitions show a more distinct difference in the effect of the introduced tokenization scheme on their LZ ratio.

关键词： Generative AI Music Tokenization Music information retrieval compression algorithms

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Analysing Lossless Image compression Techniques for IoT Devices

Analysing Lossless Image Compression Techniques for IoT Devi...

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Electrical, Electronics, Information and Communication Technologies (ICEEICT), International Conference on

作者： Kausik Challapalli Vivek Kothamasu Hasha Garikapati Roop Sai Charan ManiSankar Thota Satish Anamalamudi Computer Science and Engineering SRM University Mangalagiri AP India

ISBN: (数字)9798350369083

ISBN: (纸本)9798350369090

Lossless image compression is the technique now that allows to keep the quality high as long as the file size is being minimized to meet the needs of uploading or archiving [1]. The other category consists of Deflate whose shortcomings are fast speed and lower efficiency [2]. By considering this, smaller files size might never be the case but it is the best alternative for quality images and this forms part of formats like PNG and ZIP [3]. On the other hand, the BROTLI admirably shines with its extremely high compression ratios, when dealing with repeated pattern of information [4]. On the contrast, it pertains to the extremely resource intensive, which may eventually need more computing power, as the ratio is in favor of the compression [5]. Regardless of the chosen technique, the outcome remains consistent: for a compressed file whose data original size has been truncated, having a size that ranges from pixels to a file which can work on them without giving a distorted image, targeting those who need high-end images but small file sizes [6].

关键词： Performance evaluation Image quality Image coding Cement industry Data compression Stability analysis Information and communication technology Internet of Things Image storage compression algorithms

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QualityNet: Error-bounded Lossy compression Quality Prediction via Deep Surrogate

QualityNet: Error-bounded Lossy Compression Quality Predicti...

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IEEE International Conference on Big Data

作者： Khondoker Mirazul Mumenin Dong Dai Jinzhen Wang Sheng Di University of North Carolina Charlotte USA University of Delaware USA Argonne National Laboratory USA

ISBN: (数字)9798350362480

ISBN: (纸本)9798350362497

As scientific simulations generate increasingly large datasets, efficient data compression becomes essential to mitigate storage and transmission challenges. Error-bounded Lossy compression algorithms reduce data volume at the expense of data fidelity. However, the evaluation of compressed data quality can be resource-intensive and time-consuming. This paper proposes a surrogate-based framework to predict key compression quality assessment metrics, allowing users to evaluate the quality of compressed data quickly without extensive trial and error. We evaluate our proposed framework on real-world scientific application datasets. Our surrogate-based framework shows superior prediction accuracy, with the RMSE generally lower than 5%. On the other hand, our framework demonstrates robust generalization for prediction across data fields within the same application. Our results show that our approach significantly reduces the error bound selection time and accelerates the downstream evaluation process for decompressed data, making it easier for domain scientists to select optimal error bounds of lossy compression while maintaining data quality that meets their needs. This work bridges the gap between compression ratio optimization and data fidelity, offering a scalable solution for scientific applications that rely on large-scale lossy compression.

关键词： Measurement Adaptation models Data integrity Computational modeling Predictive models Prediction algorithms Data models Quality assessment compression algorithms Optimization

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Application of Lossless compression Method in Image compression

Application of Lossless Compression Method in Image Compress...

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International Conference on Information Systems and Computer Aided Education (ICISCAE)

作者： Weifeng Ni Tianqiu Pu Yuxi Wang College of Information Science and Engineering Hohai University Changzhou China

ISBN: (数字)9798350350760

ISBN: (纸本)9798350350777

This paper is based on the application of lossless compression algorithms in image compression, aiming to solve problems such as insufficient storage space, low transmission efficiency, and heavy data processing burden. In order to optimize the image lossless compression algorithm, a combination of Huffman encoding and LZW compression algorithm is proposed. The implementation process and advantages and disadvantages of the two are compared, and the compression ratio and encoding efficiency of image lossless compression technology are discussed. We need to optimize decoding speed, image quality, and other aspects to improve overall image lossless compression capability. By applying Huffman encoding and LZW compression algorithms to images separately, the results show that Huffman encoding can dynamically construct the optimal encoding based on the frequency of characters appearing in image data, while LZW technology is good at utilizing repeated patterns and contextual information in image data to further improve compression efficiency by establishing dictionaries. The complementary advantages of the two can further improve the lossless compression ability of images.

关键词： Image quality Deep learning Image coding Dictionaries Propagation losses Decoding Image storage Huffman coding compression algorithms Information systems

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Research on Sidelobe Suppression Algorithm of Pulse compression in Radar Signal Processing

Research on Sidelobe Suppression Algorithm of Pulse Compress...

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Chinese Automation Congress (CAC)

作者： Xu Chen Li Wang Xiaoxue Li Qiaoyu Li Yatao Liu Yong Luo University of Electronic Science and Technology of China Chengdu Sichuan China

ISBN: (数字)9798350368604

ISBN: (纸本)9798350368611

Linear frequency modulation (LFM) signal is often used for pulse compression in radar system, but there are some problems such as high sidelobe and main lobe broadening after compression, which has certain interference to the accurate detection of actual target signal. This paper discusses the bidirectional weighted ultra-low sidelobe pulse compression algorithm, and gives an improved algorithm according to its shortcomings. Based on this, the window function linear combination method is introduced, which can obtain better sidelobe suppression ability when the main lobe width is basically unchanged.

关键词： Chirp modulation Simulation Signal processing algorithms Radar detection Radar Interference Radar signal processing Faces compression algorithms Weighted sum model

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Enhanced Image Concealment: Leveraging LSB and Brotli compression

Enhanced Image Concealment: Leveraging LSB and Brotli Compre...

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Advancement in Technology (ICONAT), International Conference for

作者： Shaik Yacoob Vikrant Sabnis Jay Kumar Jain Mansarovar Global University Bhopal MP India Department of Computer Science Faculty of Engineering and Technology Mansarovar Global University Bhopal MP India Department of Mathematics Bioinformatics and Computer Applications Maulana Azad National Institute of Technology Bhopal MP India

ISBN: (数字)9798350354171

ISBN: (纸本)9798350354188

Steganography a cryptographic technique enables the concealment of confidential data within multimedia files like images and videos. This method facilitates the exchange of encrypted information through unconventional means, ensuring that only intended recipients can decipher the hidden message. The LSB technique has attracted significant attention in the literature due to its capability to conceal secret message bits within the least significant parts of image pixels. Although LSB has performed well in image steganography, there is some way to go in terms of its development, especially in the ways in which messages are dealt with. This paper presents a new technique of image steganography basing on the LSB method and the BROTLI algorithm. It uses the compression algorithm to compress message text. After that, to scramble the text, one LSB embedding technique is then used to hide the compressed text in the cover image. Applying the methods of data analysis on the benchmark images, the new approach developed outperforms the standard methods that exist at the present stage. This supports the idea of performing BROTLI on data with a view of compressing the data to be embedded in LSBs.

关键词： Measurement Steganography Image coding Data analysis Streaming media Benchmark testing compression algorithms Standards Videos

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An Overview of Applications of Data compression & Source Coding in Wireless Networks

An Overview of Applications of Data Compression & Source Cod...

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International Conference on Computing and Networking Technology (ICCNT)

作者： Aarsi Kumari Abhinav B Jayaprakash Department of Computer Science& IT ARKA JAIN University Jamshedpur Jharkhand India Department of Agriculture Vivekananda Global University Jaipur India Department of Computer Science & IT School of Sciences JAIN (Deemed to be University) Bangalore Karnataka India

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370270

Facts compression and source coding techniques are used in Wi-Fi networks for several packages. Those techniques may be used to lessen the size of transmitted information, thereby increasing the spectral performance of a community and improving the performance of the channel. In addition, fact compression can also be used for programs such as video streaming and voice verbal exchange. Records compression and supply coding strategies are often utilized in Wi-Fi networks to reduce the bit fee of a channel. By using efficient algorithms, facts may be compressed and transmitted without sacrificing first-rate. compression algorithms can be used to reduce the facts fee and throughput of a wireless network without decreasing the best of the records. Through doing so, it is feasible to reduce the size of transmitted data while growing the performance and performance of the channel. Source coding is another technique used in wireless networks to optimize the performance of the channel. Supply coding algorithms are used to lessen the bit charge of a channel with the aid of the use of various techniques together with bitsensible coding and entropy coding. Those algorithms can assist in lessening the c of records that are dispatched over the air and can also help lessen the noise components. Statistics compression and supply coding can also be used to improve the performance of video streaming and voice communiqué applications in a Wi-Fi network.

关键词： Source coding Wireless networks Noise Streaming media Throughput Entropy coding Wireless fidelity compression algorithms

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

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

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Electrical Engineering and Mechatronics Technology (ICEEMT), IEEE International Conference on

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

ISBN: (数字)9798350356472

ISBN: (纸本)9798350356489

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.

关键词： Interpolation Smoothing methods Mechatronics Fluctuations Fitting Machining Real-time systems Splines (mathematics) Junctions compression algorithms

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Optimizing Data compression via Data Reordering

Optimizing Data Compression via Data Reordering

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Collaborative Advances in Software and COmputiNg (CASCON), International Conference on

作者： Qinxin Du Xiaohui Yu Aijun An Dariusz Jania York University Toronto ON Canada IBM Cloud and Cognitive Software Warszawa Poland

ISBN: (数字)9798331504830

ISBN: (纸本)9798331515621

Data compression is critical for efficient data operations such as migration, backup, and analytical processing. While there has been significant research into compression techniques for multimedia like videos and images, relational data has garnered less attention. Current methods for compressing relational data, such as gzip and LZ4, do not optimize record ordering within tables, potentially missing opportunities for improved compression. It has been shown that grouping similar records together can enhance compression performance, as measured by the compression ratio. As such, we propose two novel record reordering methods: (1) clustering and (2) solving a Travelling Salesman Problem (TSP) to reorder records. Additionally, we apply locality-sensitive hashing (LSH) on textual columns to convert text into vector representations, aiding in clustering and TSP reordering. For columns with a fewer number of distinct values, using run-length encoding (RLE) rather than traditional dictionary-based compression may be more effective. We therefore extend our approach to partition the set of columns in a table into two groups based on the distinct value count and then use appropriate compression algorithms for each, aiming to maximize the overall compression ratio. We conducted extensive experiments with one synthetic and three real datasets to evaluate our proposed methods. The results confirm that our proposed approaches significantly outperform existing baselines, demonstrating their efficacy in relational data compression.

关键词： Image coding Current measurement Data compression Collaboration Streaming media Vectors Encoding Software compression algorithms Videos

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FPGA-Accelerated Hybrid Lossless and Lossy compression for Next-Generation Portable Optoacoustic Platforms

FPGA-Accelerated Hybrid Lossless and Lossy Compression for N...

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IEEE International Symposium on Applications of Ferroelectrics (ISAF)

作者： Federico Villani Sevrin Mathys Çağla Özsoy Xosé Luís Deán-Ben Andrea Cossettini Michele Magno Daniel Razansky Luca Benini Department of Information Technology and Electrical Engineering ETH Zurich Switzerland Institute for Biomedical Engineering and Institute of Pharmacology and Toxicology University of Zurich and ETH Zurich Switzerland

ISBN: (数字)9798350371901

ISBN: (纸本)9798350371918

Optoacoustic (OA) imaging combines optical excitation and ultrasound beamforming to render images of deep tissues with functional contrast and high spatial and temporal resolution; however, its high data rates and large channel count pose significant challenges to developing portable OA systems with limited bandwidth and computational resources. In this context, on-device data compression is required to maximize the information throughput. Traditional lossless compression (LLC) preserves image fidelity but is characterized by non-constant compression ratios that can result in bandwidth saturation. On the other hand, lossy compression (LC) achieves higher and controlled compression at the expense of image quality. This work presents an FPGA-based hybrid compression algorithm that dynamically combines LLC and LC to adapt to bandwidth constraints in real-time, ensuring consistent data transmission while preserving critical image features. Implemented on a Xilinx Kria K26 FPGA, the algorithm can compress 400 MSamples/s, processing multiple analog-to-digital converter channels per compute unit with low FPGA resource utilization (27.1k registers and 35.1 Look-up tables). The hybrid algorithm achieves an average compression ratio of 5.3 ± 0.3 in LLC mode and up to 11 ± 0.1 when the recovery mode activates LC, validated on 6,000 frames of a human OA dataset. Image quality comparisons on human vasculature datasets show that key anatomical details are preserved even during LC. The proposed approach prevents bandwidth saturation without significant hardware overhead, supporting the development of next-generation portable OA imaging systems with efficient and low-cost I/O interfaces.

关键词： Image quality Image coding Heuristic algorithms Bandwidth Real-time systems Hardware Resource management Next generation networking Field programmable gate arrays compression algorithms

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