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Automatic filter coefficient calculation in lifting scheme wavelet transform for lossless image compression / Ignacio Hernández-Bautista in The Visual Computer, vol 37 n° 5 (May 2021)  

Public [article]  inThe Visual Computer > vol 37 n° 5 (May 2021) . - pp 957 - 972 Titre : Automatic filter coefficient calculation in lifting scheme wavelet transform for lossless image compression Type de document : Article/Communication Auteurs : Ignacio Hernández-Bautista, Auteur ; Jesús Ariel Carrasco-Ochoa, Auteur ; José Francisco Martínez-Trinidad, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : pp 957 - 972 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] analyse spectrale [Termes IGN] classification par réseau neuronal convolutif [Termes IGN] compression d'image [Termes IGN] décomposition spectrale [Termes IGN] transformation en ondelettes Résumé : (auteur) In this paper, a new method for automatic filter coefficient calculation in lifting scheme wavelet transform for image lossless compression is proposed. Actually, there is no specific rule for setting filter coefficients (a, b). Therefore, this work proposes an automatic method to calculate the filter coefficients depending on the spectral analysis of each image. Also, filter coefficients are determined for five decomposition levels and for each quadrant through applying the discrete wavelet transform in the lossless image compression problem. Spectral patterns are computed and fixed into small length vectors for building different wavelet decomposition levels; these vectors are automatically computed using a 1-NN classifier. Experimental results over standard images show that calculating the wavelet filter coefficients using the proposed method generates higher compression rates (in entropy and bitstream values) against standard wavelet and linear prediction filters. Numéro de notice : A2021-398 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.1007/s00371-020-01846-0 Date de publication en ligne : 24/04/2020 En ligne : https://doi.org/10.1007/s00371-020-01846-0 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97693 [article]

Satellite systems / Tien M. Nguyen (2021)  

Public Titre : Satellite systems : design, modeling, simulation and analysis Type de document : Monographie Auteurs : Tien M. Nguyen, Éditeur scientifique Editeur : London [UK] : IntechOpen Année de publication : 2021 ISBN/ISSN/EAN : 978-1-83968-374-9 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Technologies spatiales [Termes IGN] compression d'image [Termes IGN] données GNSS [Termes IGN] données spatiotemporelles [Termes IGN] image à haute résolution [Termes IGN] image optique [Termes IGN] microsatellite [Termes IGN] télédétection spatiale [Termes IGN] température de surface de la mer [Termes IGN] véhicule spatial Index. décimale : 21.00 Technologies spatiales Résumé : (Editeur) This book provides a high-level overview of the current state of the art and future of satellite systems, satellite control systems, and satellite systems design. Chapters cover such topics as existing and future satellite systems, satellite communication subsystems, space control and Space Situation Awareness (SAA), machine learning methods with novel neural networks, data measurements in Global Navigation Satellite Systems, and much more. This volume is a practical reference for system engineers, design engineers, system analysts, and researchers in satellite engineering and advanced mathematical modeling fields. Note de contenu : 1. Communication Subsystems for Satellite Design By Hung H. Nguyen and Peter S. Nguyen 2. Overview of Existing and Future Advanced Satellite Systems By John Nguyen 3. Game Theoretic Training Enabled Deep Learning Solutions for Rapid Discovery of Satellite Behaviors By Dan Shen, Carolyn Sheaff, Genshe Chen, Jingyang Lu, Mengqing Guo, Erik Blasch and Khanh Pham 4. Future Satellite System Architectures and Practical Design Issues: An Overview By Tien M. Nguyen 5. System Designs of Microsatellites: A Review of Two Schools of Thoughts By Triharjanto Robertus 6. Design of Intelligent and Open Avionics System Onboard By Changqing Wu, Xiaodong Han and Yakun Wang 7. Dynamic Link from Liftoff to Final Orbital Insertion for a MEO Space Vehicle By Jack K. Kreng and Gleason Q. Chen 8. Effective Algorithms for Detection Outliers and Cycle Slip Repair in GNSS Data Measurements By Igor V. Bezmenov 9. Analysis of Spatiotemporal Variability of Surface Temperature of Okhotsk Sea and Adjacent Waters Using Satellite Data By Dmitry Lozhkin 10. Compression of High-Resolution Satellite Images Using Optical Image Processing By Anirban Patra, Arijit Saha, Debasish Chakraborty and Kallol Bhattacharya Numéro de notice : 26712 Affiliation des auteurs : non IGN Thématique : IMAGERIE/POSITIONNEMENT Nature : Recueil / ouvrage collectif DOI : 10.5772/intechopen.73789 Date de publication en ligne : 14/04/2021 En ligne : https://doi.org/10.5772/intechopen.73789 Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99491

Learning-based hyperspectral imagery compression through generative neural networks / Chubo Deng in Remote sensing, vol 12 n° 21 (November 2020)  

Public [article]  inRemote sensing > vol 12 n° 21 (November 2020) . - n° 3657 Titre : Learning-based hyperspectral imagery compression through generative neural networks Type de document : Article/Communication Auteurs : Chubo Deng, Auteur ; Yi Cen, Auteur ; Lifu Zhang, Auteur Année de publication : 2020 Article en page(s) : n° 3657 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] analyse en composantes principales [Termes IGN] apprentissage profond [Termes IGN] compression d'image [Termes IGN] compression par ondelettes [Termes IGN] image hyperspectrale [Termes IGN] réseau neuronal artificiel Résumé : (auteur) Hyperspectral images (HSIs), which obtain abundant spectral information for narrow spectral bands (no wider than 10 nm), have greatly improved our ability to qualitatively and quantitatively sense the Earth. Since HSIs are collected by high-resolution instruments over a very large number of wavelengths, the data generated by such sensors is enormous, and the amount of data continues to grow, HSI compression technique will play more crucial role in this trend. The classical method for HSI compression is through compression and reconstruction methods such as three-dimensional wavelet-based techniques or the principle component analysis (PCA) transform. In this paper, we provide an alternative approach for HSI compression via a generative neural network (GNN), which learns the probability distribution of the real data from a random latent code. This is achieved by defining a family of densities and finding the one minimizing the distance between this family and the real data distribution. Then, the well-trained neural network is a representation of the HSI, and the compression ratio is determined by the complexity of the GNN. Moreover, the latent code can be encrypted by embedding a digit with a random distribution, which makes the code confidential. Experimental examples are presented to demonstrate the potential of the GNN to solve image compression problems in the field of HSI. Compared with other algorithms, it has better performance at high compression ratio, and there is still much room left for improvements along with the fast development of deep-learning techniques. Numéro de notice : A2020-720 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.3390/rs12213657 Date de publication en ligne : 08/11/2020 En ligne : https://doi.org/10.3390/rs12213657 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96310 [article]

Convolutional Neural Networks for embedded vision / Lucas Fernandez Brillet (2020)  

Public Titre : Convolutional Neural Networks for embedded vision Titre original : Réseaux de neurones CNN pour la vision embarquée Type de document : Thèse/HDR Auteurs : Lucas Fernandez Brillet, Auteur ; Stéphane Mancini, Directeur de thèse Editeur : Grenoble [France] : Université Grenoble Alpes Année de publication : 2020 Importance : 164 p. Format : 21 x 30 cm Note générale : bibliographie Thèse pour obtenir le grade de Docteur de l'Université Grenoble Alpes, Spécialité : Mathématiques, sciences et technologies de l’information, informatique Langues : Français (fre) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] analyse en composantes principales [Termes IGN] classification par réseau neuronal convolutif [Termes IGN] compression d'image [Termes IGN] détection d'objet [Termes IGN] image à haute résolution [Termes IGN] instrument embarqué [Termes IGN] vision par ordinateur [Termes IGN] zone d'intérêt Index. décimale : THESE Thèses et HDR Résumé : (auteur) Recently, Convolutional Neural Networks have become the state-of-the-art soluion(SOA) to most computer vision problems. In order to achieve high accuracy rates, CNNs require a high parameter count, as well as a high number of operations. This greatly complicates the deployment of such solutions in embedded systems, which strive to reduce memory size. Indeed, while most embedded systems are typically in the range of a few KBytes of memory, CNN models from the SOA usually account for multiple MBytes, or even GBytes in model size. Throughout this thesis, multiple novel ideas allowing to ease this issue are proposed. This requires to jointly design the solution across three main axes: Application, Algorithm and Hardware.In this manuscript, the main levers allowing to tailor computational complexity of a generic CNN-based object detector are identified and studied. Since object detection requires scanning every possible location and scale across an image through a fixed-input CNN classifier, the number of operations quickly grows for high-resolution images. In order to perform object detection in an efficient way, the detection process is divided into two stages. The first stage involves a region proposal network which allows to trade-off recall for the number of operations required to perform the search, as well as the number of regions passed on to the next stage. Techniques such as bounding box regression also greatly help reduce the dimension of the search space. This in turn simplifies the second stage, since it allows to reduce the task’s complexity to the set of possible proposals. Therefore, parameter counts can greatly be reduced.Furthermore, CNNs also exhibit properties that confirm their over-dimensionment. This over-dimensionement is one of the key success factors of CNNs in practice, since it eases the optimization process by allowing a large set of equivalent solutions. However, this also greatly increases computational complexity, and therefore complicates deploying the inference stage of these algorithms on embedded systems. In order to ease this problem, we propose a CNN compression method which is based on Principal Component Analysis (PCA). PCA allows to find, for each layer of the network independently, a new representation of the set of learned filters by expressing them in a more appropriate PCA basis. This PCA basis is hierarchical, meaning that basis terms are ordered by importance, and by removing the least important basis terms, it is possible to optimally trade-off approximation error for parameter count. Through this method, it is possible to compress, for example, a ResNet-32 network by a factor of ×2 both in the number of parameters and operations with a loss of accuracy Note de contenu : Introduction 1- Deep learning overview 2- Methodology to adapt the computational complexity of CNN-based object detection for efficient inference in an applicative use-case 3- CNN compression 4- Cascaded and compressed CNNs for fast and lightweight face detection 5- Hardware evaluation on embedded multiprocessor Thesis Conclusion & Perspectives Numéro de notice : 28392 Affiliation des auteurs : non IGN Thématique : IMAGERIE/INFORMATIQUE Nature : Thèse française Note de thèse : Thèse de Doctorat : Mathématiques, sciences et technologies de l’information, informatique : Grenoble : 2020 DOI : sans En ligne : https://tel.archives-ouvertes.fr/tel-03101523/document Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=98739

Sparse signal modeling: Application to image compression, Image error concealment and compressed sensing / Ali Akbari (2018)  

Public Titre : Sparse signal modeling: Application to image compression, Image error concealment and compressed sensing Type de document : Thèse/HDR Auteurs : Ali Akbari, Auteur Editeur : Paris : Sorbonne Université Année de publication : 2018 Importance : 158 p. Format : 21 x 30 cm Note générale : bibliographie Thèse de Doctorat de Traitement du signal et de l’image, Sorbonne Université Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement du signal [Termes IGN] acquisition comprimée [Termes IGN] compensation [Termes IGN] compression d'image [Termes IGN] modélisation [Termes IGN] problème inverse [Termes IGN] reconstruction d'image [Termes IGN] représentation parcimonieuse [Termes IGN] théorie du signal Résumé : (auteur) Signal models are a cornerstone of contemporary signal and image processing methodology. In this report, two particular signal modeling methods, called analysis and synthesis sparse representation, are studied which have been proven to be effective for many signals, such as natural images, and successfully used in a wide range of applications. Both models represent signals in terms of linear combinations of an underlying set, called dictionary, of elementary signals known as atoms. The driving force behind both models is sparsity of the representation coefficients, i.e. the rapid decay of the representation coefficients over the dictionary. On the other hands, the dictionary choice determines the success of the entire model. According to these two signal models, there have been two main disciplines of dictionary designing; harmonic analysis approach and machine learning methodology. The former leads to designing the dictionaries with easy and fast implementation, while the latter provides a simple and expressive structure for designing adaptable and efficient dictionaries. The main goal of this thesis is to provide new applications to these signal modeling methods by addressing several problems from various perspectives. It begins with the direct application of the sparse representation, i.e. image compression. The line of research followed in this area is the synthesis-based sparse representation approach in the sense that the dictionary is not fixed and predefined, but learned from training data and adapted to data, yielding a more compact representation. A new Image codec based on adaptive sparse representation over a trained dictionary is proposed, wherein different sparsity levels are assigned to the image patches belonging to the salient regions, being more conspicuous to the human visual system. Experimental results show that the proposed method outperforms the existing image coding standards, such as JPEG and JPEG2000, which use an analytic dictionary, as well as the state-of-the-art codecs based on the trained dictionaries. In the next part of thesis, it focuses on another important application of the sparse signal modeling, i.e. solving inverse problems, especially for error concealment (EC), wherein a corrupted image is reconstructed from the incomplete data, and Compressed Sensing recover, where an image is reconstructed from a limited number of random measurements. Signal modeling is usually used as a prior knowledge about the signal to solve these NP-hard problems. In this thesis, inspired by the analysis and synthesis sparse models, these challenges are transferred into two distinct sparse recovery frameworks and several recovery methods are proposed. Compared with the state-of-the-art EC and CS algorithms, experimental results show that the proposed methods show better reconstruction performance in terms of objective and subjective evaluations. This thesis is finalized by giving some conclusions and introducing some lines for future works. Note de contenu : 1- Introduction 2- Sparsity-based signal models 3- Image compressed sensing recovery 4- Receiver-based error concealment based on synthesis sparse recovery 5- Transmitter-based error concealment based on sparse recovery 6- Sparse representation-based image compression 7- Conclusion and future directions Numéro de notice : 25937 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Thèse française Note de thèse : Thèse de Doctorat : Spécialité : Traitement du signal et de l’image : Paris : 2018 nature-HAL : Thèse DOI : sans En ligne : http://www.theses.fr/2018SORUS461 Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96286

Statistical atmospheric parameter retrieval largely benefits from spatial–spectral image compression / Joaquín García-Sobrino in IEEE Transactions on geoscience and remote sensing, vol 55 n° 4 (April 2017)  

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Adaptive spectral–spatial compression of hyperspectral image with sparse representation / Wei Fu in IEEE Transactions on geoscience and remote sensing, vol 55 n° 2 (February 2017)  

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Quality assessment methodologies of post-processed images / Lukas Krasula (2017)  

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Retina-inspired image and video coding / Effrosyni Doutsi (2017)  

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Dictionary learning for promoting structured sparsity in hyperspectral compressive sensing / Lei Zhang in IEEE Transactions on geoscience and remote sensing, vol 54 n° 12 (December 2016)  

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Compressive sensing for multibaseline polarimetric SAR tomography of forested areas / Xinwu Li in IEEE Transactions on geoscience and remote sensing, vol 54 n° 1 (January 2016)  

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A computational introduction to digital image processing / Alasdair McAndrew (2016)

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HYCA: A new technique for hyperspectral compressive sensing / G. Martin in IEEE Transactions on geoscience and remote sensing, vol 53 n° 5 (mai 2015)  

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Wavelet-Based Compressed Sensing for SAR Tomography of Forested Areas / Esteban Aguilera in IEEE Transactions on geoscience and remote sensing, vol 51 n° 12 (December 2013)  

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A sparse image fusion algorithm with application to pan-sharpening / Xiao Xiang Zhu in IEEE Transactions on geoscience and remote sensing, vol 51 n° 5 Tome 1 (May 2013)  

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