Auswahl der wissenschaftlichen Literatur zum Thema „AI-based compression“
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Zeitschriftenartikel zum Thema "AI-based compression"
Sarinova, Assiya, und Alexander Zamyatin. „Methodology for Developing Algorithms for Compressing Hyperspectral Aerospace Images used on Board Spacecraft“. E3S Web of Conferences 223 (2020): 02007. http://dx.doi.org/10.1051/e3sconf/202022302007.
Der volle Inhalt der QuelleSarinova, Assiya. „Development of compression algorithms for hyperspectral aerospace images based on discrete orthogonal transformations“. E3S Web of Conferences 333 (2021): 01011. http://dx.doi.org/10.1051/e3sconf/202133301011.
Der volle Inhalt der QuelleKim, Myung-Jun, und Yung-Lyul Lee. „Object Detection-Based Video Compression“. Applied Sciences 12, Nr. 9 (29.04.2022): 4525. http://dx.doi.org/10.3390/app12094525.
Der volle Inhalt der QuellePinheiro, Antonio. „JPEG column: 89th JPEG meeting“. ACM SIGMultimedia Records 12, Nr. 4 (Dezember 2020): 1. http://dx.doi.org/10.1145/3548580.3548583.
Der volle Inhalt der QuelleSarinova, Assiya, Pavel Dunayev, Aigul Bekbayeva, Ali Mekhtiyev und Yermek Sarsikeyev. „Development of compression algorithms for hyperspectral aerospace images based on discrete orthogonal transformations“. Eastern-European Journal of Enterprise Technologies 1, Nr. 2(115) (25.02.2022): 22–30. http://dx.doi.org/10.15587/1729-4061.2022.251404.
Der volle Inhalt der QuelleNagarsenker, Anish, Prasad Khandekar und Minal Deshmukh. „JPEG2000-Based Semantic Image Compression using CNN“. International journal of electrical and computer engineering systems 14, Nr. 5 (05.06.2023): 527–34. http://dx.doi.org/10.32985/ijeces.14.5.4.
Der volle Inhalt der QuellePetraikin, A. V., Zh E. Belaya, A. N. Kiseleva, Z. R. Artyukova, M. G. Belyaev, V. A. Kondratenko, M. E. Pisov et al. „Artificial intelligence for diagnosis of vertebral compression fractures using a morphometric analysis model, based on convolutional neural networks“. Problems of Endocrinology 66, Nr. 5 (25.12.2020): 48–60. http://dx.doi.org/10.14341/probl12605.
Der volle Inhalt der QuelleJane, Robert, Tae Young Kim, Samantha Rose, Emily Glass, Emilee Mossman und Corey James. „Developing AI/ML Based Predictive Capabilities for a Compression Ignition Engine Using Pseudo Dynamometer Data“. Energies 15, Nr. 21 (28.10.2022): 8035. http://dx.doi.org/10.3390/en15218035.
Der volle Inhalt der QuelleMa, Xiaoqian. „Analysis on the Application of Multimedia-Assisted Music Teaching Based on AI Technology“. Advances in Multimedia 2021 (27.12.2021): 1–12. http://dx.doi.org/10.1155/2021/5728595.
Der volle Inhalt der QuelleBai, Ye, Fei Bo, Wencan Ma, Hongwei Xu und Dawei Liu. „Effect of Interventional Therapy on Iliac Venous Compression Syndrome Evaluated and Diagnosed by Artificial Intelligence Algorithm-Based Ultrasound Images“. Journal of Healthcare Engineering 2021 (22.07.2021): 1–8. http://dx.doi.org/10.1155/2021/5755671.
Der volle Inhalt der QuelleDissertationen zum Thema "AI-based compression"
Berthet, Alexandre. „Deep learning methods and advancements in digital image forensics“. Electronic Thesis or Diss., Sorbonne université, 2022. http://www.theses.fr/2022SORUS252.
Der volle Inhalt der QuelleThe volume of digital visual data is increasing dramatically year after year. At the same time, image editing has become easier and more precise. Malicious modifications are therefore more accessible. Image forensics provides solutions to ensure the authenticity of digital visual data. Recognition of the source camera and detection of falsified images are among the main tasks. At first, the solutions were classical methods based on the artifacts produced during the creation of a digital image. Then, as in other areas of image processing, the methods moved to deep learning. First, we present a state-of-the-art survey of deep learning methods for image forensics. Our state-of-the-art survey highlights the need to apply pre-processing modules to extract artifacts hidden by image content. We also highlight the problems concerning image recognition evaluation protocols. Furthermore, we address counter-forensics and present compression based on artificial intelligence, which could be considered as an attack. In a second step, this thesis details three progressive evaluation protocols that address camera recognition problems. The final protocol, which is more reliable and reproducible, highlights the impossibility of state-of-the-art methods to recognize cameras in a challenging context. In a third step, we study the impact of compression based on artificial intelligence on two tasks analyzing compression artifacts: tamper detection and social network recognition. The performances obtained show on the one hand that this compression must be taken into account as an attack, but that it leads to a more important decrease than other manipulations for an equivalent image degradation
Desai, Ujjaval Y., Marcelo M. Mizuki, Ichiro Masaki und Berthold K. P. Horn. „Edge and Mean Based Image Compression“. 1996. http://hdl.handle.net/1721.1/5943.
Der volle Inhalt der QuelleBuchteile zum Thema "AI-based compression"
Li, Ge, Wei Gao und Wen Gao. „MPEG AI-Based 3D Graphics Coding Standard“. In Point Cloud Compression, 219–41. Singapore: Springer Nature Singapore, 2024. http://dx.doi.org/10.1007/978-981-97-1957-0_10.
Der volle Inhalt der QuelleFalk, Eric. „AI to Solve the Data Deluge: AI-Based Data Compression“. In Future of Business and Finance, 271–85. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-41309-5_18.
Der volle Inhalt der QuelleBecking, Daniel, Maximilian Dreyer, Wojciech Samek, Karsten Müller und Sebastian Lapuschkin. „ECQ$$^{\text {x}}$$: Explainability-Driven Quantization for Low-Bit and Sparse DNNs“. In xxAI - Beyond Explainable AI, 271–96. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-04083-2_14.
Der volle Inhalt der QuelleOni-Orisan, Oluwatomiwa. „Viability of Knowledge Management Practices for a Successful Digital Transformation in Small- and Medium- Sized Enterprises“. In Informatik aktuell, 129–39. Wiesbaden: Springer Fachmedien Wiesbaden, 2024. http://dx.doi.org/10.1007/978-3-658-43705-3_10.
Der volle Inhalt der QuelleSandhya, Mandha, und G. Mallikarjuna Rao. „Prediction of Compressive Strength of Fly Ash-Based Geopolymer Concrete Using AI Approach“. In Lecture Notes in Civil Engineering, 9–20. Singapore: Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-16-8496-8_2.
Der volle Inhalt der QuelleDayal, Sankalp. „AI on Edge: A Mass Accessible Tool for Decision Support Systems“. In Decision Support Systems (DSS) and Tools [Working Title]. IntechOpen, 2024. http://dx.doi.org/10.5772/intechopen.1003945.
Der volle Inhalt der QuelleOommen, B. John, und Luis Rueda. „Stochastic Learning-based Weak Estimation and Its Applications“. In Knowledge-Based Intelligent System Advancements, 1–29. IGI Global, 2011. http://dx.doi.org/10.4018/978-1-61692-811-7.ch001.
Der volle Inhalt der QuelleChakraborty, Sanjay, und Lopamudra Dey. „Image Representation, Filtering, and Natural Computing in a Multivalued Quantum System“. In Handbook of Research on Natural Computing for Optimization Problems, 689–717. IGI Global, 2016. http://dx.doi.org/10.4018/978-1-5225-0058-2.ch028.
Der volle Inhalt der QuelleFratrič, Peter, Giovanni Sileno, Tom van Engers und Sander Klous. „A Compression and Simulation-Based Approach to Fraud Discovery“. In Frontiers in Artificial Intelligence and Applications. IOS Press, 2022. http://dx.doi.org/10.3233/faia220463.
Der volle Inhalt der QuelleKonferenzberichte zum Thema "AI-based compression"
Bergmann, Sandra, Denise Moussa, Fabian Brand, André Kaup und Christian Riess. „Frequency-Domain Analysis of Traces for the Detection of AI-based Compression“. In 2023 11th International Workshop on Biometrics and Forensics (IWBF). IEEE, 2023. http://dx.doi.org/10.1109/iwbf57495.2023.10157489.
Der volle Inhalt der QuelleStroot, Markus, Stefan Seiler, Philipp Lutat und Andreas Ulbig. „Comparative Analysis of Modern, AI-based Data Compression on Power Quality Disturbance Data“. In 2023 IEEE International Conference on Communications, Control, and Computing Technologies for Smart Grids (SmartGridComm). IEEE, 2023. http://dx.doi.org/10.1109/smartgridcomm57358.2023.10333901.
Der volle Inhalt der QuelleBerthet, Alexandre, Chiara Galdi und Jean-Luc Dugelay. „On the Impact of AI-Based Compression on Deep Learning-Based Source Social Network Identification“. In 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP). IEEE, 2023. http://dx.doi.org/10.1109/mmsp59012.2023.10337726.
Der volle Inhalt der QuelleBerthet, Alexandre, und Jean-Luc Dugelay. „AI-Based Compression: A New Unintended Counter Attack on JPEG-Related Image Forensic Detectors?“ In 2022 IEEE International Conference on Image Processing (ICIP). IEEE, 2022. http://dx.doi.org/10.1109/icip46576.2022.9897697.
Der volle Inhalt der QuelleJane, Robert, Corey James, Samantha Rose und Tae Kim. „Developing Artificial Intelligence (AI) and Machine Learning (ML) Based Soft Sensors for In-Cylinder Predictions with a Real-Time Simulator and a Crank Angle Resolved Engine Model“. In WCX SAE World Congress Experience. 400 Commonwealth Drive, Warrendale, PA, United States: SAE International, 2023. http://dx.doi.org/10.4271/2023-01-0102.
Der volle Inhalt der QuelleMisra, Siddharth, Jungang Chen, Polina Churilova und Yusuf Falola. „Generative Artificial Intelligence for Geomodeling“. In International Petroleum Technology Conference. IPTC, 2024. http://dx.doi.org/10.2523/iptc-23477-ms.
Der volle Inhalt der QuelleMa, jialin, hang hu und Yuetong Zhang. „Reconfigurable pulse-compression signal generation based on DP-QPSK modulator“. In AI in Optics and Photonics, herausgegeben von Juejun Hu und Jianji Dong. SPIE, 2023. http://dx.doi.org/10.1117/12.3007024.
Der volle Inhalt der QuelleKang, Haoyan, Jiachi Ye, Behrouz Movahhed Nouri, Belal Jahannia, Salem Altaleb, Hao Wang, Elham Heidari, Volker Sorger und Hamed Dalir. „Reconfigurable complex convolution module based optical data compression and hashing algorithm“. In AI and Optical Data Sciences V, herausgegeben von Volker J. Sorger und Ken-ichi Kitayama. SPIE, 2024. http://dx.doi.org/10.1117/12.3003411.
Der volle Inhalt der QuelleThambi, Joel Luther, Subhransu Sekhar Mohapatra, Vinod Jose Kavalakkat, Subhransu S. Mohapatra, Ullas U und Saibal Kanchan Barik. „A Combined Data Science and Simulation-Based Methodology for Efficient and Economic Prediction of Thermoplastic Performance for Automotive Industry“. In WCX SAE World Congress Experience. 400 Commonwealth Drive, Warrendale, PA, United States: SAE International, 2023. http://dx.doi.org/10.4271/2023-01-0936.
Der volle Inhalt der QuelleAkita, Eiji, Shin Gomi, Scott Cloyd, Michael Nakhamkin und Madhukar Chiruvolu. „The Air Injection Power Augmentation Technology Provides Additional Significant Operational Benefits“. In ASME Turbo Expo 2007: Power for Land, Sea, and Air. ASMEDC, 2007. http://dx.doi.org/10.1115/gt2007-28336.
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