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Survey of Inter-Prediction Methods for Time-Varying Mesh compression

COMPUTER GRAPHICS FORUM 2025年第1期44卷

作者： Dvorak, Jan Hacha, Filip Arvanitis, Gerasimos Podgorelec, David Moustakas, Konstantinos Vasa, Libor Univ West Bohemia Pilsen Fac Appl Sci Dept Comp Sci & Engn Plzen Czech Republic Univ Patras Dept Elect & Comp Engn Patras Greece Univ Maribor Fac Elect Engn & Comp Sci Maribor Slovenia

Time-varying meshes (TVMs), that is mesh sequences with varying connectivity, are a greatly versatile representation of shapes evolving in time, as they allow a surface topology to change or details to appear or disappear at any time during the sequence. This, however, comes at the cost of large storage size. Since 2003, there have been attempts to compress such data efficiently. While the problem may seem trivial at first sight, considering the strong temporal coherence of shapes represented by the individual frames, it turns out that the varying connectivity and the absence of implicit correspondence information that stems from it makes it rather difficult to exploit the redundancies present in the data. Therefore, efficient and general TVM compression is still considered an open problem. We describe and categorize existing approaches while pointing out the current challenges in the field and hint at some related techniques that might be helpful in addressing them. We also provide an overview of the reported performance of the discussed methods and a list of datasets that are publicly available for experiments. Finally, we also discuss potential future trends in the field.

关键词： compression algorithms data compression modelling polygonal mesh reduction

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A Low-Complexity and High-Throughput Hardware Design for Lempel-Ziv 4 compression Algorithm

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IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS I-REGULAR PAPERS 2025年

作者： Chen, Tao Song, Suwen Wang, Zhongfeng Nanjing Univ Sch Elect Sci & Engn Nanjing 210023 Peoples R China Sun Yat Sen Univ Sch Integrated Circuits Shenzhen 518107 Peoples R China

The Lempel-Ziv (LZ) 4 compression algorithm, widely used in data transmission and storage, faces the challenge of high-speed implementation and increased complexity in the era of big data. Therefore, this paper proposes a single-core parallel architecture for LZ4 algorithm with high throughput and low complexity. Firstly, to enhance throughput, two innovative approaches are introduced from the perspective of parallelism and frequency with an acceptable compression ratio loss: each parallelization window is restricted to performing a single match, bridging the gap between actual and theoretical parallelism;the feedback loop in the circuit is broken by utilizing the spatial correlation between adjacent matches for higher frequency. Secondly, two optimization schemes are employed on resource-consuming modules to achieve low complexity. Multi-port hash tables using Live Value Table (LVT) are improved based on inherent data characteristics, significantly reducing the hardware resource consumption while ensuring excellent scalability on hash table depth and frequency. The match comparison operation is moved ahead, further reducing the logic resources by 64.36%. Finally, our design is implemented on FPGA and ASIC platforms. Experimental results on FPGA demonstrate that the proposed architecture achieves a throughput of 17.39 Gb/s, exhibiting a 2.86x improvement over the state-of-the-art, along with a 6.46x enhancement in area efficiency. Further optimizations including Canonic Signed Digit (CSD) coding and computational reuse on the ASIC platform result in a 45x improvement in area efficiency.

关键词： Hardware Throughput Parallel processing Complexity theory Field programmable gate arrays compression algorithms Encoding Signal processing algorithms Parallel architectures Logic Lossless data compression LZ4 algorithm FPGA ASIC high throughput low complexity hardware accelerator

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Quality Scores compression of Genomic Sequencing Data: A Comprehensive Review and Performance Evaluation

IEEE Transactions on Computational Biology and Bioinformatic...

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IEEE Transactions on Computational Biology and Bioinformatics 2025年第2期22卷 817-831页

作者： Yuansheng Liu Tao Tang Zexuan Zhu Xiangxiang Zeng Quan Zou Keqin Li College of Information Science and Engineering Hunan University Changsha Hunan China School of Mordern Posts Nanjing University of Posts and Telecommunications Nanjing Jiangsu China College of Computer Science and Software Engineering Shenzhen University Shenzhen China Institute of Fundamental and Frontier Sciences University of Electronic Science and Technology of China Chengdu China National Supercomputing Center Changsha Hunan China Department of Computer Science State University of NY New Paltz NY USA

Advanced sequencing technologies have profoundly revolutionized biology and produced vast amounts of raw sequencing data during the past decades. The enormous amount of sequencing data proposed significant challenges of data storage and transmission. Compressing a big file into a small file is an encouraging method to tackle these challenges. Howver, it has been found that traditional text data compression algorithms are not well-suited for handing the vast sequencing datasets. Therefore, several algorithms are designed specifically for the efficient compression of sequencing data. Recently, considerable research has been devoted to compressing quality scores stored in the FASTQ format file, resulting in substantial advances in compression performance. Despite these advances, there has been no systematic review and evaluation of these algorithms or software. In this review, we aim to conduct a broad review of the existing quality score compression algorithms. We mainly discuss those algorithms from two categories, i.e., lossless and lossy compression. Additionally, we benchmark the compression performance of 12 tools using 14 real datasets. We anticipate that our review will provide practical guidance for others seeking to design an appropriate algorithm for compressing quality scores.

关键词： Sequential analysis Genomics Bioinformatics compression algorithms Data compression Image coding Electronic mail Data mining Computational biology Training

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IMAGE compression VIA IMPROVED QUADTREE DECOMPOSITION algorithms

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IEEE TRANSACTIONS ON IMAGE PROCESSING 1994年第2期3卷 207-215页

作者： SHUSTERMAN, E FEDER, M Department of Electrical Engineering-Systems Faculty of Engineering Tel-Aviv University Tel-Aviv Israel

Quadtree decomposition is a simple technique used to obtain an image representation at different resolution levels. This representation can be useful for a variety of image processing and image compression algorithms. This paper presents a simple way to get better compression performances (in MSE sense) via quadtree decomposition, by using: Near to optimal choice of the threshold for quadtree decomposition. Bit allocation procedure based on the equations derived from rate-distortion theory. The rate-distortion performance of the improved algorithm is calculated for some Gaussian field, and it is examined vie simulation over benchmark gray-level images. In both these cases, significant improvement in the compression performances is shown.

关键词： Image coding Image segmentation Rate-distortion Image representation Image resolution Image processing Bit rate compression algorithms Equations Image sequences

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Multi-Component Temporal-Correlation Seismic Data compression Algorithm Based on the PCA and DWT

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algorithms 2025年第1期18卷 33-33页

作者： de Lucena, Mateus Martinez Ribeiro, Josafat Leal Wagner, Matheus Frohlich, Antonio Augusto Univ Fed Santa Catarina Software Hardware Integrat Lab BR-88040900 Florianopolis Brazil UFSC CTC LISHA POB 476 BR-88040900 Florianopolis Brazil

Industrial application data acquisition systems can be sources of vast amounts of data. The seismic surveys conducted by oil and gas companies result in enormous datasets, often exceeding terabytes of data. The storage and communication demands these data require can only be achieved through compression. Careful consideration must be given to minimize the reconstruction error of compressed data caused by lossy compression. This paper investigates the combination of principal component analysis (PCA), discrete wavelet transform (DWT), thresholding, quantization, and entropy encoding to compress such datasets. The proposed method is a lossy compression algorithm tuned by evaluating the reconstruction error in frequency ranges of interest, namely 0-20 Hz and 15-65 Hz. The PCA compression and decompression acts as a noise filter while the DWT drives the compression. The proposed method can be tuned through threshold and quantization percentages and the number of principal components to achieve compression rates of up to 31:1 with reconstruction residues energy of less than 4% in the frequency ranges of 0-20 Hz, 15-65 Hz, and 60-105 Hz.

关键词： compression algorithms principal component analysis discrete wavelet transform

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Processing algorithms for three-dimensional data compression of ultrasonic radio frequency signals

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IET SIGNAL PROCESSING 2015年第3期9卷 267-276页

作者： Govindan, Pramod Saniie, Jafar IIT Dept Elect & Comp Engn Chicago IL 60616 USA

Ultrasonic systems are widely used in imaging applications for non-destructive evaluation, quality assurance and medical diagnosis. These applications require large volumes of data to be processed, stored and/or transmitted in real-time. Therefore it is essential to compress the acquired ultrasonic radio frequency (RF) signal without inadvertently degrading desirable signal features. In this paper, two algorithms for ultrasonic signal compression are analysed based on: sub-band elimination using discrete wavelet transform;and decimation/interpolation using time-shift property of Fourier transform. Both algorithms offer high signal reconstruction quality with a peak signal-to-noise ratio (PSNR) between 36 to 39 dB for minimum 80% compression. The computational loads and signal reconstruction quality are examined in order to determine the best compression method in terms of the choice of DWT kernel, sub-band decomposition architecture and computational efficiency. Furthermore, for compressing a large amount of volumetric information, three-dimensional (3D) compression algorithms are designed by utilising the temporal and spatial correlation properties of the ultrasonic RF signals. The performance analysis indicates that the 3D compression algorithm presented in this paper offers an overall 3D compression ratio of 95% with a minimum PSNR of 27 dB.

关键词： radiofrequency compression algorithms subband Data compression radio frequency signal large volume data Peak signal-to-noise ratio Signal reconstruction discrete wavelet transform processing algorithm

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compression Algorithm of Convolutional Neural Network Model

Compression Algorithm of Convolutional Neural Network Model

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Cybersecurity, Internet of Things and Soft Computing (CITSC), Asia-Europe Conference on

作者： Yanchun Liu Lishuang Zhao College of Information Science and Technology Bohai University Jinzhou China

ISBN: (数字)9798331504205

ISBN: (纸本)9798331504212

The exponential growth of data requires powerful analytical methods and processing tools, and deep learning demonstrates powerful analytical and predictive capabilities. CNN is excellent model in the field of deep learning, enabling powerful feature abstraction and image understanding that goes beyond the accuracy of recognition using the human eye. However, while the CNN model is constantly approaching the precision limit, the network depth and model parameters have multiplied, and the huge memory consumption and excessive storage occupation cannot be applied on many embedded and mobile devices. This paper constructs a compression algorithm model, including convolution layer, filter and pruning algorithm, and focuses on structured pruning and filter importance calculation. Using MNIST data set, the LeNet-5 model was trained by parameter pruning according to different pruning ratios, and the model compression was realized. The application of the research results in this paper can significantly reduce the redundant parameters in the network model, thus achieving the purpose of reducing the computing cost and storage space, and promoting the development of deep learning.

关键词： Deep learning Training Costs Accuracy Computational modeling Memory management Filtering algorithms Mobile handsets Convolutional neural networks compression algorithms

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A Novel Artistic Image compression Algorithm Based on Multi-Scale Adaptive Discrete Wavelet Transform

A Novel Artistic Image Compression Algorithm Based on Multi-...

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Multi-Agent Systems for Collaborative Intelligence (ICMSCI), International Conference on

作者： Yijun Li China Academy of Art Yuhang District Hangzhou Zhejiang China

ISBN: (数字)9798331509828

ISBN: (纸本)9798331509835

How to compress information is a challenge in the digital age, and the image compression has a wide range of application scenarios. In order to solve the challenges of low compression efficiency and high loss rate of the traditional algorithms, this study proposes a compression algorithm based on multi-scale adaptive discrete wavelet transform. The core of the proposed algorithm is to make reasonable use of the multiresolution analysis characteristics of discrete wavelet transform. Based on the feature extraction, the image information is decomposed into high-frequency and low-frequency parts. This operation can stably and effectively implement frequency domain threshold processing and reduce redundant data. The proposed algorithm optimizes the encoding efficiency of image features to the greatest extent by introducing block difference features and designing a new iterative function system. In view of the high redundancy and high complexity of the artistic images, the traditional wavelet analysis is improved and optimized, and a multi-scale adaptive model is introduced. In the experiment, the test used multiple sets of artistic image test sets and compared them with mainstream algorithms (JPEG, VAE, etc.). In PSNR analysis, the proposed algorithm achieves better performance.

关键词： Image coding Wavelet transforms Transforms Discrete wavelet transforms Feature extraction Wavelet analysis Wavelet domain compression algorithms Frequency-domain analysis Collaborative intelligence

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Behavior Expression Based Policy compression Scheme for Cloud-Edge Collaborative Network

Behavior Expression Based Policy Compression Scheme for Clou...

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IEEE Conference on Wireless Communications and Networking

作者： Sijia Li Weihong Wu Jiahao Li Jiang Liu Beijing University of Posts and Telecommunications Beijing China University of Electronic Science and Technology Chengdu China Purple Mountain Laboratories Nanjing China

ISBN: (数字)9798350368369

ISBN: (纸本)9798350368376

The excessively long Segment Identifier (SID) list in SRv6 challenges the promotion of SRv6 network slicing in cloudedge collaborative network. In this paper, we studied the problem of packet overhead during SRv6 SID processing in SRv6 network slicing services. We first design a new mechanism called Behavior Expression Slicing (BES) mechanism to establish the feasibility of dynamically virtual address allocation as well as SID compression based on adjacency information. Then we propose an algorithm to optimize compression efficiency in proposed BES mechanism. The simulation results indicate that the compression rate of the proposed mechanism and algorithm can reach over 75%.

关键词： 6G mobile communication Cloud computing Federated learning Network slicing Heuristic algorithms Simulation Collaboration Resource management compression algorithms

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Considerations and algorithms for compression of Sets

Considerations and Algorithms for Compression of Sets

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Data compression Conference (DCC)

作者： Larsson, N. Jesper IT Univ Copenhagen DK-2300 Copenhagen Denmark

ISBN: (纸本)9781467360371

We consider compression of unordered sets of distinct elements, focusing particularly on compressing sets of fixed-length bit strings in the presence of statistical information. We address previous work, and outline a novel compression algorithm that allows transparent incorporation of various estimates for probability distribution. Experiments allow the conclusion that set compression can benefit from incorporating statistics, using our method or variants of previously known techniques.

关键词： compression compression algorithms algorithms SETTING statistical information variants fixed length Unordered

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