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Region-based remote sensing image compression in wavelet domain using free angle segmentation model

ELECTRONICS LETTERS 2002年第22期38卷 1335-1337页

作者： Giusto, DD Murroni, M Petrou, M Univ Cagliari Dept Elect & Elect Engn CNIT MCLab I-09123 Cagliari Italy Univ Surrey Sch Elect Comp & Math Guildford GU2 7XH Surrey England

A novel compression algorithm suitable for remote sensing applications is proposed. Exploiting the high density of edge points, textured regions are identified and removed from the encoding process. The rest of the image is encoded in the wavelet domain. Experimental results show significant improvements compared to standard coding approaches.

关键词： Angle Data compression edge pixel compression High density compression algorithms Image compression remote sensing application Wavelet domain remote sensing images

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Lossless compression of digital audio

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IEEE SIGNAL PROCESSING MAGAZINE 2001年第4期18卷 21-32页

作者： Hans, M Schafer, RW Hewlett Packard Labs Client & Media Syst Lab Palo Alto CA USA Georgia Inst Technol Sch Elect & Comp Engn Ctr Signal & Image Proc Atlanta GA 30332 USA

The use of lossless audio coding of stereo CD quality digital audio signals sampled at 44.1 kHz for digital music distribution over Internet was described. The classification of the current state-of-the-art loseless audio compression algorithms to remove local dependency of audio samples and a description of a lossless audio coder, AudioPaK were presented. A prediction model based on finite impulse response (FIR) filters was used to analyze the interchannel decorrelation block.

关键词： Audio compression compression algorithms Internet Signal processing algorithms Audio coding Multiple signal classification Audio recording Testing Speech Transform coding

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An Index-Based Provenance compression Scheme for Identifying Malicious Nodes in Multihop IoT Network

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IEEE INTERNET OF THINGS JOURNAL 2020年第5期7卷 4061-4071页

作者： Liu, Zhe Wu, Yuting Nanjing Univ Aeronaut & Astronaut Coll Comp Sci & Technol Nanjing 210016 Peoples R China State Key Lab Cryptol Beijing 100878 Peoples R China

Data provenance is an effective method to evaluate data reliability and has become a research hotspot in recent years. However, the size of the data provenance will increase rapidly as the number of hops experienced increases, which conflicts with the limitations of energy, storage, and resources of IoT devices. The existing DP provenance compression algorithm has a high error rate when decompressing. To solve this problem, we propose an index-based provenance compression algorithm, which adopts the idea of common substring matching, combined with path identifier and path index to represent the path information in the data provenance, thereby achieving the purpose of reducing the size of data provenance. In addition, we extend the data provenance scheme to attack detection and propose a malicious node identification method based on data provenance. The simulation results show that the proposed scheme has a high compression ratio and higher decoding accuracy and has high accuracy in malicious node identification.

关键词： Internet of Things compression algorithms Security Distributed databases Encoding Reliability Error analysis Attack detection compression data provenance security

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Marmotini: A Weight Density Adaptation Architecture With Hybrid compression Method for Spiking Neural Network

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IEEE TRANSACTIONS ON VERY LARGE SCALE INTEGRATION (VLSI) SYSTEMS 2024年第12期32卷 2293-2302页

作者： Wang, Zilin Zhong, Yi Ou, Zehong Yang, Youming Feng, Shuo Chen, Guang Cui, Xiaoxin Jia, Song Wang, Yuan Peking Univ MPW Ctr Sch Integrated Circuits Key Lab Microelect Devices & Circuits Beijing 100871 Peoples R China Peking Univ Sch Software & Microelect Beijing 102600 Peoples R China Peking Univ Beijing Adv Innovat Ctr Integrated Circuits Beijing 100871 Peoples R China

Brain-inspired spiking neural network (SNN) has recently attracted widespread interest owing to its event-driven nature and relatively low-power hardware for transmitting highly sparse binary spikes. To further improve energy efficiency, some matrix compression algorithms are used for weight storage. However, the weight sparsity of different layers varies greatly. For a multicore neuromorphic system, it is difficult for the same compression algorithm to adapt to all the layers of SNN model. In this work, we propose a weight density adaptation architecture with hybrid compression method for SNN, named Marmotini. It is a multicore heterogeneous design, including three types of cores to complete computation of different weight sparsity. Benefiting from the hybrid compression method, Marmotini minimizes the waste of neurons and weights as much as possible. Besides, for better flexibility, a reconfigurable core that can be configured to compute convolutional layer or fully connected layer is proposed. Implemented on Xilinx Kintex UltraScale XCKU115 field-programmable gate array (FPGA) board, Marmotini can operate at 150-MHz frequency, achieving 244.6-GSOP/s peak performance and 54.1-GSOP/W energy efficiency at 0% spike sparsity.

关键词： Multicore processing Neurons Computer architecture compression algorithms Indexes Convolution Synapses Convolutional neural network field-programmable gate array (FPGA) neuromorphic computing spiking neural network (SNN)

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STATISTICAL DISTRIBUTIONS OF DCT COEFFICIENTS AND THEIR APPLICATION TO AN INTERFRAME compression ALGORITHM FOR 3-D MEDICAL IMAGES

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IEEE TRANSACTIONS ON MEDICAL IMAGING 1993年第3期12卷 478-485页

作者： LEE, H KIM, YM ROWBERG, AH RISKIN, EA UNIV WASHINGTON DEPT RADIOLSEATTLEWA 98195

We present displacement estimated interframe (DEI) coding, a new coding scheme for 3-D medical image data sets such as x-ray CT or MR images. To take advantage of the correlation between contiguous slices, a displacement-compensated difference image based on the previous image is encoded. We have determined the best fitting distribution functions for the discrete cosine transform (DCT) coefficients obtained from displacement compensated difference images, and used the results in allocating bits and optimizing quantizers for the coefficients. The DEI scheme is compared with a 2-D block DCT as well as a full-frame DCT using the bit allocation technique by Lo and Huang [1]. For x-ray CT head images, our bit allocation and quantizer design using an appropriate distribution model resulted in a 13 dB improvement in the SNR compared to the full-frame DCT using the bit allocation technique. For an image set with 5 mm slice thickness, the DEI method compared to the 2-D block DCT gave about 5 percent improvement in the compression ratio on average and less blockiness at the same distortion. The performance gain increases to about 10 percent when the slice thickness decreases to 3 mm.

关键词： compression ratio difference image compression algorithms interframe discrete cosine transforms Three-Dimensional Images slicing labeling scheme quantizer statistical distribution

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FILTERING RANDOM NOISE FROM DETERMINISTIC SIGNALS VIA DATA-compression

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IEEE TRANSACTIONS ON SIGNAL PROCESSING 1995年第11期43卷 2595-2605页

作者： NATARAJAN, BK Hewlett Packard Laboratories Palo Alto CA USA

We present a novel technique for the design of filters for random noise, leading to a class of filters called Occam filters. The essence of the technique is that when a lossy data compression algorithm is applied to a noisy signal with the allowed loss set equal to the noise strength, the loss and the noise tend to cancel rather than add. We give two illustrative applications of the technique to univariate signals. We also prove asymptotic convergence bounds on the effectiveness of Occam filters.

关键词： Filtering Data compression Noise cancellation Wiener filter Nonlinear filters compression algorithms Additive noise Convergence Power measurement Noise measurement

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compression OF COLOR-MAPPED IMAGES

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IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING 1994年第3期32卷 534-541页

作者： HADENFELDT, AC SAYOOD, K UNIV NEBRASKA DEPT ELECT ENGNLINCOLNNE 68588

Multispectral data is often displayed and stored as a color-mapped or pseudo-color image. Pseudo-color is also used to enhance features in a single-band image. The use of pseudo-color tends to rearrange the structure in the image in such a way as to prevent efficient compression. This structure can be restored by sorting the color maps. Restoration of the structure increases the efficiency of lossless compression and permits the use of lossy compression algorithms. The latter benefit is especially useful for many progressive transmission algorithms.

关键词： Image coding Pixel Color Sorting Image restoration compression algorithms Image storage NASA Computer displays Image generation

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Local Decode and Update for Big Data compression

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IEEE TRANSACTIONS ON INFORMATION THEORY 2020年第9期66卷 5790-5805页

作者： Vatedka, Shashank Tchamkerten, Aslan Indian Inst Technol Dept Elect Engn Hyderabad 502285 India Inst Polytech Paris Dept Communicat & Elect Telecom Paris F-91120 Palaiseau France

This paper investigates data compression that simultaneously allows local decoding and local update. The main result is a universal compression scheme for memoryless sources with the following features. The rate can be made arbitrarily close to the entropy of the underlying source, contiguous fragments of the source can be recovered or updated by probing or modifying a number of codeword bits that is on average linear in the size of the fragment, and the overall encoding and decoding complexity is quasilinear in the blocklength of the source. In particular, the local decoding or update of a single message symbol can be performed by probing or modifying on average a constant number of codeword bits. This latter part improves over previous best known results for which local decodability or update efficiency grows logarithmically with blocklength.

关键词： Decoding Encoding Entropy Complexity theory Computational modeling Cloud computing DNA Data compression source coding compression algorithms big data applications

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Partial Gated Feedback Recurrent Neural Network for Data compression Type Classification

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IEEE ACCESS 2020年 8卷 151426-151436页

作者： Song, Hyewon Kwon, Beom Yoo, Hoon Lee, Sanghoon Yonsei Univ Dept Elect & Elect Engn Seoul 03722 South Korea Sangmyung Univ Dept Elect Seoul 03016 South Korea

Owing to the widespread use of digital devices such as mobile phones and tablet PCs that are capable of easily viewing contents, the number of digital crimes committed using these digital devices has increased. One of the most common digital crimes is to hide the header information of the compressed data, which makes the user's data unusable. It is difficult to restore original data without the header because header contains the compression type. In this paper, we propose a Partial Gated Feedback Recurrent Neural Network (PGF-RNN) for the identification of lossless compression algorithms. We modify the gated recurrent units to improve the correlation of layers by grouping the fully-connected layers to effectively determine the characteristics of the compressed data. We emphasize on the temporal features, which consider a wide range of data, and spatial features from fully-connected layers to extract the feature vectors of each compression type. To improve the performance of the proposed PGF-RNN, we apply post-processing that considers the frequency of bit sequences on some compression types with similar compressed data. The proposed method is evaluated on 31 well-known lossless compression algorithms of the Association for Computational Linguistics dataset. The average top 1 accuracy of the proposed method is 92.63%.

关键词： Feature extraction compression algorithms Logic gates Recurrent neural networks Encoding Data mining Image coding compression type classification deep learning gated recurrent unit lossless compression partial gated feedback recurrent neural network recurrent neural network

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Multi-distribution noise quantisation: an extreme compression scheme for transformer according to parameter distribution

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CONNECTION SCIENCE 2022年第1期34卷 990-1004页

作者： Yu, Zaiyang Li, Shuang Sun, Linjun Liu, Liang Wang Haining Chinese Acad Sci Inst Semicond Beijing Peoples R China Wave Grp Cognit Comp Technol Joint Lab Beijing Peoples R China Univ Chinese Acad Sci Ctr Mat Sci & Optoelect Engn Beijing 100049 Peoples R China Univ Chinese Acad Sci Sch Microelect Beijing 100049 Peoples R China Beijing Key Lab Semicond Neural Network Intellige Beijing 100083 Peoples R China Peoples Publ Secur Univ China Sch Police Adm Beijing Peoples R China

With the development of deep learning, neural networks are widely used in various fields, and the improved model performance also introduces a considerable number of parameters and computations. Model quantisation is a technique that turns floating-point computing into low-specific-point computing, which can effectively reduce model computation strength, parameter size, and memory consumption but often bring a considerable loss of accuracy. This paper mainly addresses the problem where the distribution of parameters is too concentrated during quantisation aware training (QAT). In the QAT process, we use a piecewise function to statistics the parameter distributions and simulate the effect of quantisation noise in each round of training, based on the statistical results. Experimental results show that by quantising the Transformer network, we lose less precision and significantly reduce the storage cost of the model;compared with the full precision LSTM network, our model has higher accuracy under the condition of a similar storage cost. Meanwhile, compared with other quantisation methods on language modelling task, our approach is more accurate. We validated the effectiveness of our policy on the WikiText-103 and PENN Treebank datasets. The experiments show that our method extremely compresses the storage cost and maintains high model performance.

关键词： compression algorithms natural language processing transformer vector quantisation appendices

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