Bibliografías temáticas / AI-based compression

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Literatura académica sobre el tema "AI-based compression"

Autor: Grafiati
Publicado: 25 de mayo de 2024
Última modificación: 19 de julio de 2025

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Artículos de revistas sobre el tema "AI-based compression"

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Sarinova, Assiya, and 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.

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The paper describes a method for constructing and developing algorithms for compressing hyperspectral aerospace images (AI) of hardware implementation for subsequent use in remote sensing Systems (RSS). The developed compression methods based on differential and discrete transformations are proposed as compression algorithms necessary for reducing the amount of transmitted information. The paper considers a method for developing compression algorithms, which is used to develop an adaptive algorithm for compressing hyperspectral AI using programmable devices. Studies have shown that the propose
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Sarinova, 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.

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The paper describes the development of compression algorithms for hyperspectral aerospace images based on discrete orthogonal transformations for the purpose of subsequent compression in Earth remote sensing systems. As compression algorithms necessary to reduce the amount of transmitted information, it is proposed to use the developed compression methods based on Walsh-Hadamard transformations and discrete-cosine transformation. The paper considers a methodology for developing lossy and high-quality compression algorithms during recovery, taking into account which an adaptive algorithm for co
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Kim, Myung-Jun, and Yung-Lyul Lee. "Object Detection-Based Video Compression." Applied Sciences 12, no. 9 (2022): 4525. http://dx.doi.org/10.3390/app12094525.

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Video compression is designed to provide good subjective image quality, even at a high-compression ratio. In addition, video quality metrics have been used to show the results can maintain a high Peak Signal-to-Noise Ratio (PSNR), even at high compression. However, there are many difficulties in object recognition on the decoder side due to the low image quality caused by high compression. Accordingly, providing good image quality for the detected objects is necessary for the given total bitrate for utilizing object detection in a video decoder. In this paper, object detection-based video comp
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Sarinova, Assiya, Pavel Dunayev, Aigul Bekbayeva, Ali Mekhtiyev, and Yermek Sarsikeyev. "Development of compression algorithms for hyperspectral aerospace images based on discrete orthogonal transformations." Eastern-European Journal of Enterprise Technologies 1, no. 2(115) (2022): 22–30. http://dx.doi.org/10.15587/1729-4061.2022.251404.

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The work is devoted to the description of the development of compression algorithms for hyperspectral aerospace images based on discrete orthogonal transformations for the purpose of subsequent compression in Earth remote sensing systems. As compression algorithms necessary to reduce the amount of transmitted information, it is proposed to use the developed compression methods based on Walsh-Hadamard transformations and discrete-cosine transformation. The paper considers a methodology for developing lossy and high-quality compression algorithms during recovery of 85 % or more, taking into acco
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Pinheiro, Antonio. "JPEG column: 89th JPEG meeting." ACM SIGMultimedia Records 12, no. 4 (2020): 1. http://dx.doi.org/10.1145/3548580.3548583.

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JPEG initiates standardisation of image compression based on AI. The 89th JPEG meeting was held online from 5 to 9 October 2020. During this meeting, multiple JPEG standardisation activities and explorations were discussed and progressed. Notably, the call for evidence on learning-based image coding was successfully completed and evidence was found that this technology promises several new functionalities while offering at the same time superior compression efficiency, beyond the state of the art. A new work item, JPEG AI, that will use learning-based image coding as core technology has been p
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Assiya, Sarinova, Dunayev Pavel, Bekbayeva Aigul, Mekhtiyev Ali, and Sarsikeyev Yermek. "Development of compression algorithms for hyperspectral aerospace images based on discrete orthogonal transformations." Eastern-European Journal of Enterprise Technologies 1, no. 2(115) (2022): 22–30. https://doi.org/10.15587/1729-4061.2022.251404.

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The work is devoted to the description of the development of compression algorithms for hyperspectral aerospace images based on discrete orthogonal transformations for the purpose of subsequent compression in Earth remote sensing systems. As compression algorithms necessary to reduce the amount of transmitted information, it is proposed to use the developed compression methods based on Walsh-Hadamard transformations and discrete-cosine transformation. The paper considers a methodology for developing lossy and high-quality compression algorithms during recovery of 85 % or more, taking into
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Nagarsenker, Anish, Prasad Khandekar, and Minal Deshmukh. "JPEG2000-Based Semantic Image Compression using CNN." International journal of electrical and computer engineering systems 14, no. 5 (2023): 527–34. http://dx.doi.org/10.32985/ijeces.14.5.4.

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Some of the computer vision applications such as understanding, recognition as well as image processing are some areas where AI techniques like convolutional neural network (CNN) have attained great success. AI techniques are not very frequently used in applications like image compression which are a part of low-level vision applications. Intensifying the visual quality of the lossy video/image compression has been a huge obstacle for a very long time. Image processing tasks and image recognition can be addressed with the application of deep learning CNNs as a result of the availability of lar
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Researcher. "DATA REDUCTION TECHNOLOGIES FOR AI WORKLOADS: ADVANCING COMPRESSION AND DEDUPLICATION TECHNIQUES FOR LARGE-SCALE AI/ML DATASETS." International Journal of Computer Engineering and Technology (IJCET) 15, no. 5 (2024): 960–67. https://doi.org/10.5281/zenodo.13970869.

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This comprehensive article explores the cutting-edge advancements in data reduction technologies specifically tailored for large-scale AI and machine learning workloads. As the volume and complexity of AI datasets continue to grow exponentially, traditional compression and deduplication techniques face significant challenges in efficiently managing unstructured, high-dimensional data. We examine the unique characteristics of AI/ML datasets and analyze the limitations of conventional data reduction methods when applied to these workloads. The article presents an in-depth discussion of emer
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Jeong, Woosik, Chang-Heon Baek, Dong-Yeong Lee, et al. "The Classification of Metastatic Spine Cancer and Spinal Compression Fractures by Using CNN and SVM Techniques." Bioengineering 11, no. 12 (2024): 1264. https://doi.org/10.3390/bioengineering11121264.

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Metastatic spine cancer can cause pain and neurological issues, making it challenging to distinguish from spinal compression fractures using magnetic resonance imaging (MRI). To improve diagnostic accuracy, this study developed artificial intelligence (AI) models to differentiate between metastatic spine cancer and spinal compression fractures in MRI images. MRI data from Gyeongsang National University Hospital, collected from January 2019 to April 2022, were processed using Otsu’s binarization and Canny edge detection algorithms. Using these preprocessed datasets, convolutional neural network
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Timmerer, Christian. "MPEG Column: 146th MPEG Meeting in Rennes, France." ACM SIGMultimedia Records 16, no. 2 (2024): 1. https://doi.org/10.1145/3712580.3712584.

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The 146th MPEG meeting was held in Rennes, France from 22-26 April 2024, and the official press release can be found here. It comprises the following highlights: •AI-based Point Cloud Coding: Call for proposals focusing on AI-driven point cloud encoding for applications such as immersive experiences and autonomous driving. •Object Wave Compression: Call for interest in object wave compression for enhancing computer holography transmission. •Open Font Format: Committee Draft of the fifth edition, overcoming previous limitations like the 64K glyph encoding constraint. •Scene Description: Ratifie
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Tesis sobre el tema "AI-based compression"

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Berthet, Alexandre. "Deep learning methods and advancements in digital image forensics." Electronic Thesis or Diss., Sorbonne université, 2022. http://www.theses.fr/2022SORUS252.

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Le volume de données visuelles numériques augmente considérablement d'année en années. En parallèle, l’édition d'images est devenue plus facile et plus précise. Les modifications malveillantes sont donc plus accessibles. La criminalistique des images fournit des solutions pour garantir l’authenticité des données visuelles numériques. Tout d’abord, les solutions étaient des méthodes classiques basées sur les artéfacts produits lors de la création d’une image numérique. Puis, comme pour d’autres domaines du traitement d’images, les méthodes sont passées à l’apprentissage profond. Dans un premier
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Desai, Ujjaval Y., Marcelo M. Mizuki, Ichiro Masaki, and Berthold K. P. Horn. "Edge and Mean Based Image Compression." 1996. http://hdl.handle.net/1721.1/5943.

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In this paper, we present a static image compression algorithm for very low bit rate applications. The algorithm reduces spatial redundancy present in images by extracting and encoding edge and mean information. Since the human visual system is highly sensitive to edges, an edge-based compression scheme can produce intelligible images at high compression ratios. We present good quality results for facial as well as textured, 256~x~256 color images at 0.1 to 0.3 bpp. The algorithm described in this paper was designed for high performance, keeping hardware implementation issues in m
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Capítulos de libros sobre el tema "AI-based compression"

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Li, Ge, Wei Gao, and Wen Gao. "MPEG AI-Based 3D Graphics Coding Standard." In Point Cloud Compression. Springer Nature Singapore, 2024. http://dx.doi.org/10.1007/978-981-97-1957-0_10.

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Falk, Eric. "AI to Solve the Data Deluge: AI-Based Data Compression." In Future of Business and Finance. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-41309-5_18.

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Witt, Nicolas, Mark Deutel, Jakob Schubert, Christopher Sobel, and Philipp Woller. "Energy-Efficient AI on the Edge." In Unlocking Artificial Intelligence. Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-64832-8_19.

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AbstractThis chapter shows methods for the resource-optimized design of AI functionality for edge devices powered by microprocessors or microcontrollers. The goal is to identify Pareto-optimal solutions that satisfy both resource restrictions (energy and memory) and AI performance. To accelerate the design of energyefficient classical machine learning pipelines, an AutoML tool based on evolutionary algorithms is presented, which uses an energy prediction model from assembly instructions (prediction accuracy 3.1%) to integrate the energy demand into a multiobjective optimization approach. For the deployment of deep neural network-based AI models, deep compression methods are exploited in an efficient design space exploration technique based on reinforcement learning. The resulting DNNs can be executed with a self-developed runtime for embedded devices (dnnruntime), which is benchmarked using the MLPerf Tiny benchmark. The developed methods shall enable the fast development of AI functions for the edge by providing AutoML-like solutions for classical as well as for deep learning. The developed workflows shall narrow the gap between data scientist and hardware engineers to realize working applications. By iteratively applying the presented methods during the development process, edge AI systems could be realized with minimized project risks.
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Ferreira, Gabriel Bicalho, Pedro Silva, and Rodrigo Silva. "Elevating Healthcare AI: Achieving Efficiency and Accuracy in Medical Applications with Surrogate-Based Multiobjective Compression of ResNet50 CNNs." In Lecture Notes in Computer Science. Springer Nature Switzerland, 2025. https://doi.org/10.1007/978-3-031-79038-6_10.

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Becking, Daniel, Maximilian Dreyer, Wojciech Samek, Karsten Müller, and Sebastian Lapuschkin. "ECQ$$^{\text {x}}$$: Explainability-Driven Quantization for Low-Bit and Sparse DNNs." In xxAI - Beyond Explainable AI. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-04083-2_14.

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AbstractThe remarkable success of deep neural networks (DNNs) in various applications is accompanied by a significant increase in network parameters and arithmetic operations. Such increases in memory and computational demands make deep learning prohibitive for resource-constrained hardware platforms such as mobile devices. Recent efforts aim to reduce these overheads, while preserving model performance as much as possible, and include parameter reduction techniques, parameter quantization, and lossless compression techniques.In this chapter, we develop and describe a novel quantization paradigm for DNNs: Our method leverages concepts of explainable AI (XAI) and concepts of information theory: Instead of assigning weight values based on their distances to the quantization clusters, the assignment function additionally considers weight relevances obtained from Layer-wise Relevance Propagation (LRP) and the information content of the clusters (entropy optimization). The ultimate goal is to preserve the most relevant weights in quantization clusters of highest information content.Experimental results show that this novel Entropy-Constrained and XAI-adjusted Quantization (ECQ$$^{\text {x}}$$ x ) method generates ultra low-precision (2–5 bit) and simultaneously sparse neural networks while maintaining or even improving model performance. Due to reduced parameter precision and high number of zero-elements, the rendered networks are highly compressible in terms of file size, up to 103$$\times $$ × compared to the full-precision unquantized DNN model. Our approach was evaluated on different types of models and datasets (including Google Speech Commands, CIFAR-10 and Pascal VOC) and compared with previous work.
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Sandhya, Mandha, and G. Mallikarjuna Rao. "Prediction of Compressive Strength of Fly Ash-Based Geopolymer Concrete Using AI Approach." In Lecture Notes in Civil Engineering. Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-16-8496-8_2.

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Oni-Orisan, Oluwatomiwa. "Viability of Knowledge Management Practices for a Successful Digital Transformation in Small- and Medium- Sized Enterprises." In Informatik aktuell. Springer Fachmedien Wiesbaden, 2024. http://dx.doi.org/10.1007/978-3-658-43705-3_10.

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AbstractDigital innovations and technologies, particularly artificial intelligence, offer a unique opportunity to fundamentally transform business processes. Small and medium-sized enterprises (SMEs), having a significant impact on the German economy, are encouraged to fully embrace this opportunity for digital transformation.Inspired by the usage of knowledge management as a mediation mechanism for effective AI application in [5], this case study examines its practical implications. Wiewald, an SME specializing in compressor systems, serves as an application partner in the KMI project, exploring the implementation of AI in SMEs.By comparing academic literature on Industry 4.0’s impact on knowledge management with industry experts’ perspectives, we develop an appropriate digitalization strategy based on knowledge management. Its potential implementation is discussed using Wiewald as a practical example.
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Dayal, 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.

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Artificial Intelligence (AI) advancements in last decade have been explosive and now AI is considered to be potentially surpassing human intelligence for many decision tasks like detecting objects, answering specific questions or decoding sound to speech. This also means now human can offload raw information processing to an AI enabled machine and use its outcome as their Decision Support System (DSS). To make this AI based DSS mass accessible, it has to be pervasive, cheap or free and available locally where decision is being made like a home or industry. This requires Machine Learning (ML) models powering the AI to be deployed at edge hardware like phones, security cameras, automobiles. The deployment on edge requires compressing these ML models and, in some cases, tailoring to the hardware. This chapter explains how AI is becoming an important tool for DSS and then discusses the State-of-the-Art (SOTA) model compression techniques used for deployment on edge ensuring no loss in performance.
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D, Saravanan, and S. Pragadeeswaran. "Optimized Hardware Acceleration of Deep Learning Algorithms Using Xilinx Zynq SoCs." In Smart Microcontrollers and FPGA Based Architectures for Advanced Computing and Signal Processing, 2025th ed. RADemics Research Institute, 2025. https://doi.org/10.71443/9789349552425-03.

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The growing demand for intelligent edge computing has intensified the need for deploying deep learning models on energy-efficient and performance-constrained platforms. Xilinx Zynq Systemon-Chip (SoC) devices offer a unique architectural blend of programmable logic and embedded processors, enabling customized hardware acceleration for artificial intelligence applications. This book chapter explores advanced techniques in model quantization and structured pruning to optimize neural networks for inference on Zynq-based systems. It provides an in-depth analysis of co-design strategies, layer sensitivity-aware compression, and the implementation of automation pipelines for quantization-aware training. By leveraging toolchains such as Vitis AI and PYNQ, the discussion covers complete deployment workflows from model training to hardware execution, supported by practical insights into latency, power consumption, and resource utilization. Through detailed benchmarking and performance evaluation, this work highlights how high-accuracy inference can be maintained while significantly reducing memory and compute overhead. The methodologies presented in this chapter contribute to scalable and sustainable AI acceleration solutions tailored for edge environments. The comprehensive integration of model compression, tool support, and hardware mapping provides a roadmap for efficient deployment of deep learning on reconfigurable SoCs.
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Oommen, B. John, and Luis Rueda. "Stochastic Learning-based Weak Estimation and Its Applications." In Knowledge-Based Intelligent System Advancements. IGI Global, 2011. http://dx.doi.org/10.4018/978-1-61692-811-7.ch001.

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Although the field of Artificial Intelligence (AI) has been researched for more than five decades, researchers, scientists and practitioners are constantly seeking superior methods that are applicable for increasingly difficult problems. In this chapter, our aim is to consider knowledge-based novel and intelligent cybernetic approaches for problems in which the environment (or medium) is time-varying. While problems of this sort can be approached from various perspectives, including those that appropriately model the time-varying nature of the environment, in this chapter, we shall concentrate on using new estimation or “training” methods. The new methods that we propose are based on the principles of stochastic learning, and are referred to as the Stochastic Learning Weak Estimators (SLWE). An empirical analysis on synthetic data shows the advantages of the scheme for non-stationary distributions, which is where we claim to advance the state-of-the-art. We also examine how these new methods can be applicable to learning and intelligent systems, and to Pattern Recognition (PR). The chapter briefly reports conclusive results that demonstrate the superiority of the SLWE in two applications, namely in PR and data compression. The application in PR involves artificial data and real-life news reports from the Canadian Broadcasting Corporation (CBC). We also demonstrate its applicabilty in data compression, where the underlying distribution of the files being compressed is, again, modeled as being non-stationary. The superiority of the SLWE in both these cases is demonstrated.
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Actas de conferencias sobre el tema "AI-based compression"

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Ashraf, Rizwan A., Luanzheng Guo, Hyungro Lee, and Nathan R. Tallent. "Identifying Outliers in AI-based Image Compression." In 2024 IEEE International Conference on Big Data (BigData). IEEE, 2024. https://doi.org/10.1109/bigdata62323.2024.10825013.

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Stroot, Markus, Stefan Seiler, Philipp Lutat, and Andreas Ulbig. "An AI-Based, Error-Bounded Compression Scheme for High-Frequency Power Quality Disturbance Data." In 2024 International Conference on Smart Energy Systems and Technologies (SEST). IEEE, 2024. http://dx.doi.org/10.1109/sest61601.2024.10694355.

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True, Pascal, Thomas Gräf, and Matthias Menge. "Implementation of an AI-based Compression Method for Current Waveforms of Partial Discharges in AC and DC High Voltage Systems." In 2024 IEEE International Conference on High Voltage Engineering and Applications (ICHVE). IEEE, 2024. http://dx.doi.org/10.1109/ichve61955.2024.10676181.

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Kim, Nam Seong, Young Soo Han, Young Jin Lee, Ji DOng CHOI, and Jong Jae Yoo. "Global microelectronics and laser industry trends and laser compression bonding (LCB) technology for the AI chip packaging." In Laser-based Micro- and Nanoprocessing XIX, edited by Rainer Kling, Wilhelm Pfleging, and Koji Sugioka. SPIE, 2025. https://doi.org/10.1117/12.3034090.

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Manchanda, Rachit. "An Energy-Efficient Clustering Based Data Compression in Wireless Sensor Networks Using Fuzzy Logic and Compressive Sensing." In 2024 International Conference on Artificial Intelligence and Emerging Technology (Global AI Summit). IEEE, 2024. https://doi.org/10.1109/globalaisummit62156.2024.10947990.

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Bergmann, Sandra, Denise Moussa, Fabian Brand, André Kaup, and 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.

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Stroot, Markus, Stefan Seiler, Philipp Lutat, and 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.

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Berthet, Alexandre, Chiara Galdi, and 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.

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Berthet, Alexandre, and 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.

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Abdelmalek, Mohamed, Anis Harhoura, Issam Elaloui, Mahdi Madani, and El-Bay Bourennane. "Visually Image Encryption based on Efficient Deep Learning Autoencoder." In 2nd International Conference on Education & Information Technology. Academy & Industry Research Collaboration Center, 2024. http://dx.doi.org/10.5121/csit.2024.141206.

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This paper proposes an Artificial Intelligence (AI) model based on Convolutional Neural Network (CNN) for visual image protection during encryption and decryption processes. We used the CIFAR-10 dataset containing 60.000 color images of size 32×32 across ten classes to train and test the proposed network. Our focus lies in designing a convolutional autoencoder for image compression and reconstruction, utilizing an encoder-decoder architecture. During training, the autoencoder learns to encode essential image features into a reduced-dimensional latent space and reconstructs the image from this
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