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About 10,000 papers for “Signal compression”

Signal Compression

N. Jayant · International Journal of High Speed Electronics and Systems · 1997

This article is an introduction to a special issue on signal coding and compression. We begin by defining the concepts of digital coding and audiovisual signal compression. We then describe the four dimensions of coding performance: bit rate, signal quality, processing delay and complexity. We illustrate the two basic principles of audiovisual coding, removal of signal redundancy and the matching of the quantizing system to the properties of the human perceptual system, with specific recent examples of coding algorithms. We then summarize standards for, and applications of audiovisual signal compression. A fast-emerging application is the internetworking of audiovisual information, a field that is too recent to be covered in the articles in this collection. We conclude our article by presenting our views about future research directions in the field.

Cited by 4

Signal compression

The Dictionary of Genomics, Transcriptomics and Proteomics · 2015

Cited by 0

Compression input signal.

Maytham S. Jabor (15853046), Aqeel Salman Azez (15853049), José Carlos Campelo (15853052) et al. · Figshare · 2023

<div><p>Nowadays, Wireless Sensor Networks (WSNs) are widely used for collecting, communicating, and sharing information in various applications. Due to its limited resources in terms of computation, power, battery lifetime, and memory storage for sensor nodes, it is difficult to add confidentiality and integrity security features. It is worth noting that blockchain (BC) technology is one of the most promising technologies, because it provides security, avoids centralization, and a trusted third party. However, to apply BCs in WSNs is not an easy task because BC is typically resource-hungry for energy, computation, and memory. In this paper, the additional complication of adding BC in WSNs is compensated by an energy minimization strategy, which basically depends on minimizing the processing load of generating the blockchain hash value, and encrypting and compressing the data that travel from the cluster-heads to the base station to reduce the overall traffic, leading to reduced energy per node. A specific (dedicated) circuit is designed to implement the compression technique, generate the blockchain hash values and data encryption. The compression algorithm is based on chaotic theory. A comparison of the power consumed by a WSN using a blockchain implementation with and without the dedicated circuit, illustrates that the hardware design contributes considerably to reduce the consumption of power. When simulating both approaches, the energy consumed when replacing functions by hardware decreases up to 63%.</p></div>

Cited by 0Open access

Audio signal compression

Matej Franc · Repository of FERIT Osijek · 2023

Kompresija signala omogućava kontrolu dinamičkog raspona signala. Ima nekoliko glavnih parametara koji su nabrojani i opisani u ovom radu. Također, nabrojani su i opisani glavni tipovi audio kompresora. Izrađen je model pedale za gitaru koji je prvo napravljen u programu NI Multisim, a izradu pedale odabrana je Orange Juicer pedala temeljena na Orange Squeezer pedali Dana Armstronga. Orange Juicer pedala poznata je po glatkoj kompresiji koju stvara pomoću JFET tranzistora. U radu su napravljene male izmjene na shemi radi bolje iskoristivosti efekta pedale, te je kasnije napravljen i fizički model pedale za gitaru na kojem su napravljena mjerenja i simulacije.

Cited by 0Open access

Reconstructed compression signal.

Digna María González-Otero (4854052), Jesus María Ruiz (4854040), Sofía Ruiz de Gauna (2547658) et al. · Figshare · 2018

<p>Computed compression signal is depicted in red with the overlapped GS compression signal computed by the Q-CPR technology (in blue).</p>

Cited by 0Open access

Signal Compression with Waves

Dimitrios L. Sounas · 2024

Signal compression is one of the most important operations in systems that are used to store or transmit information, since it allows efficient use of the available bandwidth or storage capacity. A typical compression system, such as those that are used in the JPEG standard, involves a linear matrix operation, like the discrete wavelet transform, followed by a series of nonlinear operations, such as quantization of the signal and application of a code. As in many other instances of signal processing systems, the matrix operation is the most computationally demanding one since its complexity scales nonlinearly with the size of the input data. Therefore, compression systems would benefit significantly if the involved matrix operation could be implemented in the analog domain and in a parallel manner without the use of active components.

Cited by 0

Compression of ECG signal

Blaschová, Eliška · Brno University of Technology Digital Library (Brno University of Technology) · 2016

Práce představuje nejznámější metody komprese, které byly zveřejněny. Komprese signálu EKG je důležitá především pro úsporu místa v paměťových zařízeních nebo pro zefektivnění přenosu dat. Využití vlnkové transformace pro kompresi je celosvětově diskutované téma, a proto se práce zaměřuje tímto směrem. Získané vlnkové koeficienty mohou být nejprve kvantovány a následně vhodnou metodou komprimovány. Existuje mnoho možností pro výběr vlnky a stupeň rozkladu, které zde budou testovány z hlediska nejefektivnější komprese signálu EKG.

Cited by 0

COMPRESSION DU SIGNAL EEG.

Safia Bensadia, Kheira Belabid · Dépôt Institutionnel de lUniversité de Tlemcen · 2015

La compression des signaux biomédicaux présente aujourd’hui un intérêt majeur pour l’archivage et la communication. Le but de ce projet est de présenter l’application de différentes méthodes de compression des Electroencéphalogramme (EEG) par transformée, afin de trouver la meilleure méthode adaptée aux EEG. Quatre méthodes de compression de l’EEG par transformée sont étudiées : méthode basée sur la Transformée de Fourier Rapide (FFT), méthode basée sur la Transformée en Cosinus Discret (DCT), méthode basée sur la Transformée en Sinus Discrète (DST) et méthode basée sur la Transformée en ondelette Discrète (DWT). Chacune des méthodes est brièvement exposée et les résultats obtenus sur des signaux réels sont présentés en fonction du Taux de Compression (CR) et de la mesure de la racine carrée de l’erreur en pourcentage (PRD). Les résultats obtenus montrent que la méthode basée sur DWT s’avère être plus performante que les autres méthodes.

Cited by 0

Signal expansions for compression

Martin Vetterli · Infoscience (Ecole Polytechnique Fédérale de Lausanne) · 2002

Signal expansions play a key role in practical compression schemes, from audio/image/video coding standards to current adaptives bases. Recent developments, especially related to wavelets series expansions, are reviewed, and current work on "best bases" is discussed.

Cited by 2Open access

Losless EEG signal compression

Arda Gumusalan, Ziya Arnavut, Hüseyin Koçak · 2012

In this work, we study the lossless compression of EEG (electroencephalograph) signals using linear prediction and arithmetic coder. We show that, when we separate the less significant bits of each signal, linear prediction techniques yield better prediction, and with a structured arithmetic coder not only our technique achieves better compression rates than other techniques reported previously, but also our technique is much faster than the others.

Cited by 2

Topological Graph Signal Compression

Guillermo Bernárdez, Lev Telyatnikov, Eduard Alarcón et al. · arXiv (Cornell University) · 2023

Recently emerged Topological Deep Learning (TDL) methods aim to extend current Graph Neural Networks (GNN) by naturally processing higher-order interactions, going beyond the pairwise relations and local neighborhoods defined by graph representations. In this paper we propose a novel TDL-based method for compressing signals over graphs, consisting in two main steps: first, disjoint sets of higher-order structures are inferred based on the original signal --by clustering $N$ datapoints into $K\ll N$ collections; then, a topological-inspired message passing gets a compressed representation of the signal within those multi-element sets. Our results show that our framework improves both standard GNN and feed-forward architectures in compressing temporal link-based signals from two real-word Internet Service Provider Networks' datasets --from $30\%$ up to $90\%$ better reconstruction errors across all evaluation scenarios--, suggesting that it better captures and exploits spatial and temporal correlations over the whole graph-based network structure.

Cited by 1Open access

Compression du signal vidéo

Christian-Jacques RICHARD · Électronique · 1986

1 Rappels sur le signal TV numérique couleur 2 Principes de la compression de débit 3 Codage statistique (codage de Huffman) 4 Codages psychovisuels (différentiel, par blocs, par plages, par trans-formation, à rafraîchissement conditionnel) 5 Conclusion Index bibliographique

Cited by 0

Signal compression for digital television.

Huy S. Truong · eSpace (Curtin University) · 1999

Still image and image sequence compression plays an important role in the development of digital television. Although various still image and image sequence compression algorithms have already been developed, it is very difficult for them to achieve both compression performance and coding efficiency simultaneously due to the complexity of the compression process itself. As a results, improvements in the forms of hybrid coding, coding procedure refinement, new algorithms and even new coding concepts have been constantly tried, some offering very encouraging results.In this thesis, Block Adaptive Classified Vector Quantisation (BACVQ) has been developed as an alternative algorithm for still image compression. It is found that BACVQ achieves good compression performance and coding efficiency by combining variable block-size coding and classified VQ. Its performance is further enhanced by adopting both spatial and transform domain criteria for the image block segmentation and classification process. Alternative algorithms have also been developed to accelerate normal codebook searching operation and to determine the optimal sizes of classified VQ sub-codebooks.For image sequence compression, an adaptive spatial/temporal compression algorithm has been developed which divides an image sequence into smaller groups of pictures (GOP) using adaptive scene segmentation before BACVQ and variable block-size motion compensated predictive coding are applied to the intraframe and interframe coding processes. It is found the application of the proposed adaptive scene segmentation algorithm, an alternative motion estimation strategy and a new progressive motion estimation algorithm enables the performance and efficiency of the compression process to be improved even further.Apart from improving still image and image sequence compression algorithms, the application of parallel processing to image sequence compression is also investigated. Parallel image compression offers a more effective approach than the sequential counterparts to accelerate the compression process and bring it closer to real-time operation. In this study, a small scale parallel digital signal processing platform has been constructed for supporting parallel image sequence compression operation. It consists of a 486DX33 IBM/PC serving as a master processor and two DSP (PC-32) cards as parallel processors. Because of the independent processing and spatial arrangement natures of most image processing operations, an effective parallel image sequence compression algorithm has been developed on the proposed parallel processing platform to significantly reduce the processing time of the proposed parallel image compression algorithms.

Cited by 0Open access