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Overview of Variable Rate Coding in JPEG AI

IEEE Transactions on Circuits and Systems for Video Technology 2025年

作者： Jia, Panqi Brand, Fabian Yu, Dequan Karabutov, Alexander Alshina, Elena Kaup, Andre Friedrich-Alexander University Erlangen-Nürnberg Department of Electrical-Electronic-Communication Engineering Chair of Multimedia Communications and Signal Processing Erlangen91058 Germany Huawei Munich Research Center Munich80992 Germany

Empirical evidence has demonstrated that learning-based image compression can outperform classical compression frameworks. This has led to the ongoing standardization of learned-based image codecs, namely Joint Photographic Experts Group (JPEG) AI. The objective of JPEG AI is to enhance compression efficiency and provide a software and hardware-friendly solution. Based on our research, JPEG AI represents the first standardization that can facilitate the implementation of a learned image codec on a mobile device. This article presents an overview of the variable rate coding functionality in JPEG AI, which includes three variable rate adaptations: a three-dimensional quality map, a fast bit rate matching algorithm, and a training strategy. The variable rate adaptations offer a continuous rate function up to 2.0 bpp, exhibiting a high level of performance, a flexible bit allocation between different color components, and a region of interest function for the specified use case. The evaluation of performance encompasses both objective and subjective results. With regard to the objective bit rate matching, the main profile with low complexity yielded a 13.1% BD-rate gain over VVC intra, while the high profile with high complexity achieved a 19.2% BD-rate gain over VVC intra. The BD-rate result is calculated as the mean of the seven perceptual metrics defined in the JPEG AI common test conditions. With respect to subjective results, the example of improving the quality of the region of interest is illustrated. © 1991-2012 IEEE.

关键词： Image compression

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Fast and convergence-guaranteed algorithm for linear separation

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Science China(Information Sciences) 2010年第4期53卷 729-737页

作者： ZHANG David Center for Multimedia Signal Processing/Department of Computing Hong Kong Polytechnic University

Efficient linear separation algorithms are important for pattern classification applications. In this paper, an algorithm is developed to solve linear separation problems in n-dimensional space. Its convergence feature is proved. The proposed algorithm is proved to converge to a correct solution whenever the two sets are separable. The complexity of the proposed algorithm is analyzed, and experiments on both randomly generated examples and real application problems were carried out. While analysis shows that its time complexity is lower than SVM that needs computations for quadratic programming optimization, experiment results show that the developed algorithm is more efficient than the least-mean-square (LMS), and the Perceptron.

关键词： linear separation problem classification convergence efficiency complexity

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MCFTNet: Multimodal Cross-layer Fusion Transformer Network for Hyperspectral and LiDAR Data Classification

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IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 2025年 18卷 12803-12818页

作者： Huang, Wei Wu, Tianren Zhang, Xueyu Li, Liangliang Lv, Ming Jia, Zhenhong Zhao, Xiaobin Ma, Hongbing Vivone, Gemine Xinjiang University School of Computer Science and Technology Urumqi830017 China Xinjiang University Key Laboratory of Signal Detection and Processing Urumqi830017 China Guangxi University School of Computer and Electronic Information Nanning530004 China Guangxi University Guangxi Key Laboratory of Multimedia Communications and Network Technology Nanning530004 China Beijing Institute of Technology School of Information and Electronics Beijing100081 China University of Science and Technology Beijing School of Computer and Communication Engineering Beijing100083 China Tsinghua University Department of Electronic Engineering Beijing100084 China National Research Council Tito85050 Italy NBFC National Biodiversity Future Center Palermo90133 Italy

Remote sensing image classification is a popular yet challenging field. Many researchers have combined convolutional neural networks (CNNs) and Transformers for hyperspectral image (HSI) classification tasks. However, in traditional Transformers, shallow-level information does not propagate well to deeper layers, which can lead to spatial variations and overfitting. Moreover, traditional Transformer models use an external classification (CLS) token that is randomly initialized and often struggles to generalize effectively. In this paper, we combine the strengths of HSI and light detection and ranging (LiDAR) data, using LiDAR as an external CLS token, which significantly enhances classification accuracy and reliability. We propose a new multimodal cross-layer fusion transformer network (MCFTNet), integrating CNNs with the latest Transformer networks. It includes a CNN for extracting spatial features and a hybrid cross-patch attention mechanism (HyCrossAttention) for land cover classification, leveraging LiDAR data to generate CLS and HSI patch tokens. More importantly, to reduce the loss of valuable information during the layer-by-layer propagation, we designed cross-layer skip connections. Through adaptive learning, these cross-layer fusions help address the gradient vanishing problem in deep networks while preserving early-layer features. This enables the model to better integrate information from different layers, enhancing both its stability and performance. We carried out in - depth experiments on commonly - used benchmark datasets, specifically, the University of Houston dataset, the Trento dataset, and the University of Southern Mississippi Gulf Park (MUUFL) dataset. We compared the results of the proposed MCFTNet model with those obtained from state-of-the-art Transformer models, classical CNNs, and traditional classifiers. As a result, the MCFTNet model outshone them all in terms of performance. © 2008-2012 IEEE.

关键词： Convolutional neural networks

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The NeRF Signature: Codebook-Aided Watermarking for Neural Radiance Fields

arXiv

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arXiv 2025年

作者： Luo, Ziyuan Rocha, Anderson Shi, Boxin Guo, Qing Li, Haoliang Wan, Renjie Department of Computer Science Hong Kong Baptist University Hong Kong Institute of Computing University of Campinas Brazil State Key Laboratory of Multimedia Information Processing and National Engineering Research Center of Visual Technology School of Computer Science Peking University Beijing100871 China A*STAR Singapore Department of Electrical Engineering City University of Hong Kong Hong Kong

Neural Radiance Fields (NeRF) have been gaining attention as a significant form of 3D content representation. With the proliferation of NeRF-based creations, the need for copyright protection has emerged as a critical issue. Although some approaches have been proposed to embed digital watermarks into NeRF, they often neglect essential model-level considerations and incur substantial time overheads, resulting in reduced imperceptibility and robustness, along with user inconvenience. In this paper, we extend the previous criteria for image watermarking to the model level and propose NeRF Signature, a novel watermarking method for NeRF. We employ a Codebook-aided Signature Embedding (CSE) that does not alter the model structure, thereby maintaining imperceptibility and enhancing robustness at the model level. Furthermore, after optimization, any desired signatures can be embedded through the CSE, and no fine-tuning is required when NeRF owners want to use new binary signatures. Then, we introduce a joint pose-patch encryption watermarking strategy to hide signatures into patches rendered from a specific viewpoint for higher robustness. In addition, we explore a Complexity-Aware Key Selection (CAKS) scheme to embed signatures in high visual complexity patches to enhance imperceptibility. The experimental results demonstrate that our method outperforms other baseline methods in terms of imperceptibility and robustness. The source code is available at: https://***/luo-ziyuan/NeRF_Signature. Copyright © 2025, The Authors. All rights reserved.

关键词： Image watermarking

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Fast Surface Error Diagnosis for Radiotelescopes Using Linearized Microwave Holography

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IEEE Transactions on Antennas and Propagation 2025年

作者： Romero, Celia Fonta Varela, Fernando Rodriguez Iraguen, Belen Galocha Lopez-Perez, Jose-Antonio Universidad Politécnica de Madrid Information Processing and Telecommunications Center Radiation Group Department of Signals Systems and Radiocommunications MadridE-28040 Spain Universidad Rey Juan Carlos Group of Microwave Engineering and Radiocommunication Systems Department of Signal Theory and Communications and Telematic Systems and Computing MadridE-28942 Spain Centro Astronómico de Yebes Dirección General del Instituto Geográfico Nacional Guadalajara19001 Spain

Radiotelescopes are typically composed of large reflector antennas located outdoors. These antennas are designed to achieve high levels of gain, which is possible due to their large size and RF systems designed for working at high frequencies. However, various sources of error (such as mounting imperfection or deformations caused by gravity and weather effects) are difficult to identify and can significantly degrade the performance of the radiotelescope, reducing its gain. The correct diagnosis of these errors has been a challenge in radioastronomy. The most widely used technique is microwave holography, in which mechanical errors are detected through long electromagnetic acquisitions. The measured far-field is processed to obtain the aperture field and thus the errors affecting the surface or the phase distribution. In this paper, a novel methodology is proposed to diagnose reflector errors using holography. The proposed approach defines a system that models all possible aperture errors as a combination of basis functions, directly relating the errors to the acquired far-field and reducing the number of samples required for analysis. © 1963-2012 IEEE.

关键词： Reflection

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Chaotic dynamics, topological analysis and flat analog electronic control by physiological signals of a neurally-inspired system

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Chinese Journal of Physics 2025年 96卷 20-52页

作者： Tabekoueng Njitacke, Zeric Ngamsa Tegnitsap, Joakim Vianney Zhao, Manyu Barà, Chiara Fozin, Théophile Fonzin Awrejcewicz, Jan Yoshimura, Natsue Valdes-Sosa, Pedro A. Letellier, Christophe Minati, Ludovico School of Life Science and Technology University of Electronic Science and Technology of China Chengdu 611731 China Department of Electrical and Electronic Engineering College of Technology (COT) University of Buea P.O. Box 63 Buea Cameroon Fotso-Victor University Institute of Technology University of Dschang P.O. Box 134 Dschang Cameroon Research Unit of Condensed Matter Electronics and Signal Processing Department of Physics University of Dschang Cameroon Department of Engineering University of Palermo Palermo 90128 Italy Department of Electrical and Electronic Engineering Faculty of Engineering and Technology (FET) University of Buea P.O. Box 63 Buea Cameroon Department of Automation Biomechanics and Mechatronics Łódź University of Technology Stefanowskiego Str. 1/15 Lodz 90-924 Poland School of Computing Institute of Science Tokyo (formerly Tokyo Institute of Technology) Yokohama Japan ATR Brain Information Communication Research Laboratory Group Kyoto Japan Department of Neuroinformatics Cuban Neuroscience Center Havana 11600 Cuba CORIA Rouen Normandie University Saint-Etienne du Rouvray 76800 France Center for Mind/Brain Sciences (CIMeC) University of Trento Trento 38123 Italy

A novel abstract bicompartmental neural model based on a cubic nonlinearity, consisting of introducing a Josephson junction as a coupling element, is presented. Through numerical simulations and experimental measurements involving an analog electronic circuit, it is found that the system can generate a double-scroll chaotic attractor endowed with a remarkably complex topology. This is accompanied by diversification of the spectral signature across its four variables and multistability. An analysis of the global observability and controllability is conducted, leading to the synthesis of a flat controller that can steer the dynamics towards those of structurally different entities, as exemplified by a modified Lorenz system. The first analog realization of flat control is then introduced. The possibility of low-latency and high-accuracy manipulation of the dynamics is demonstrated by considering the challenging case of tracking extraneous external signals, namely, physiological recordings indexing autonomic and central nervous system activity. Besides showing the flexibility and adaptability of the approach, the controller enables the concomitant generation of multiple additional signals with spectrogram features compared to the individual inputs, offering a promising substrate for future applications in physical time series data augmentation. © 2025 The Physical Society of the Republic of China (Taiwan)

关键词： Chaotic circuits Flat control Josephson junction Multistability Neural models Physiological recordings Topological analysis

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Feature-Consistent Cyclic Domain Adversarial Network for Unsupervised Transfer Diagnosis of the Autoimmune Disease Igg4-Rd by Raman Spectroscopy

SSRN

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SSRN 2025年

作者： Zhang, Ziyang Chen, Cheng Qin, Wentao Wu, Xue Liu, Yang Wu, Lijun Lv, Xiaoyi Chang, Chenjie Chen, Xinya Chen, Zhiyuan Xu, Qianxi Li, Zhuoyu Ma, Mengsi Chen, Chen Xinjiang University College of Software Urumqi830046 China Xinjiang Cloud Computing Application Laboratory Xinjiang University Karamay834099 China School of Computer Science and Technology Xinjiang University Urumqi830046 China Key Laboratory of signal detection and processing Xinjiang University Urumqi830046 China Department of Rheumatology＆Immunology People's Hospital of Xinjiang Uygur Autonomous Region Xinjiang Urumqi China Xinjiang Clinical Research Center for Rheumatoid arthritis Xinjiang Urumqi China

Abstracts IgG4-related disease (IgG4-RD) is a multi-organ immune disorder characterized by systemic involvement, diverse pathogenesis, and rarity, which complicates its diagnosis. Traditional medical diagnostic models, predominantly based on supervised learning in machine learning or deep learning frameworks, struggle to address the scarcity of labeled data due to the diagnostic challenges of IgG4-RD. To overcome this limitation, this study proposes a novel framework based on Feature-Consistent Cyclic Domain Adversarial Networks (FCCDAN). The framework first trains a classification model on the source domain for diagnosing source domain diseases under supervised conditions. Subsequently, leveraging the source domain model, the encoder is guided to extract semantic features, while feature consistency loss is integrated into both the generator and discriminator to align the semantic features of the target domain with those of the source domain. Finally, the aligned features are processed through a pre-trained linear mapping to achieve unsupervised classification and diagnosis in the target domain. The proposed model utilizes common autoimmune diseases as a knowledge source, enabling unsupervised transfer diagnosis for IgG4-RD, and demonstrates generalizability through experiments on other disease datasets, achieving an average diagnostic accuracy of 90.62%. This represents improvements of 4.14%, 9.82%, and 10.51% over baseline models, respectively. FCCDAN expands the applicability of Raman spectroscopy in disease diagnosis, maintaining high performance even in label-scarce environments. The overall research workflow also establishes a robust paradigm for the application and advancement of domain adaptation techniques in this context. © 2025, The Authors. All rights reserved.

关键词： Adversarial machine learning

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Information technology applications in biomedical functional imaging

引用

IEEE Transactions on Information Technology in Biomedicine 1999年第3期3卷 221-230页

作者： Feng, Dagan Biomedical and Multimedia Information Technology Group Department of Computer Science University of Sydney NSW 2006 Australia Center for Multimedia Signal Processing Department of Electronic and Information Engineering Hong Kong Polytechnic University Hong Kong

In parallel with rapid advances in computer technology, biomedical functional imaging is having an ever-increasing impact on healthcare. Functional imaging allows us to see dynamic processes quantitatively in the living human body. However, as we need to deal with four-dimensional time-varying images, space requirements and computational complexity are extremely high. This makes information management, processing, and communication difficult. Using the minimum amount of data to represent the required information, developing fast algorithms to process the data, organizing the data in such a way as to facilitate information management, and extracting the maximum amount of useful information from the recorded data have become important research tasks in biomedical information technology. For the last ten years, the Biomedical and multimedia Information Technology (BMIT) Group and, recently, the center for multimedia signal processing have conducted systematic studies on these topics. Some of the results relating to functional imaging data acquisition, compression, storage, management, processing, modeling, and simulation are briefly reported in this paper.

关键词： Medical imaging

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A semi-parametric hybrid neural model for nonlinear blind signal separation.

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International journal of neural systems 2000年第2期10卷 79-93页

作者： Peng, H. Chi, Z. Siu, W. Center for Multimedia Signal Processing Department of Electronic &amp Information Engineering The Hong Kong Polytechnic University Kowloon Hong Kong

Nonlinear blind signal separation is an important but rather difficult problem. Any general nonlinear independent component analysis algorithm for such a problem should specify which solution it tries to find. Several recent neural networks for separating the post nonlinear blind mixtures are limited to the diagonal nonlinearity, where there is no cross-channel nonlinearity. In this paper, a new semi-parametric hybrid neural network is proposed to separate the post nonlinearly mixed blind signals where cross-channel disturbance is included. This hybrid network consists of two cascading modules, which are a neural nonlinear module for approximating the post nonlinearity and a linear module for separating the predicted linear blind mixtures. The nonlinear module is a semi-parametric expansion made up of two sub-networks, one of which is a linear model and the other of which is a three-layer perceptron. These two sub-networks together produce a "weak" nonlinear operator and can approach relatively strong nonlinearity by tuning parameters. A batch learning algorithm based on the entropy maximization and the gradient descent method is deduced. This model is successfully applied to a blind signal separation problem with two sources. Our simulation results indicate that this hybrid model can effectively approach the cross-channel post nonlinearity and achieve a good visual quality as well as a high signal-to-noise ratio in some cases.

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A comparative study on kernel-based probabilistic neural networks for speaker verification.

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International journal of neural systems 2002年第5期12卷 381-397页

作者： Yiu, K.K. Mak, M.W. Kung, S.Y. Center for Multimedia Signal Processing Department of Electronic and Information Engineering The Hong Kong Polytechnic University Hung Hom Hong Kong

This paper compares kernel-based probabilistic neural networks for speaker verification based on 138 speakers of the YOHO corpus. Experimental evaluations using probabilistic decision-based neural networks (PDBNNs), Gaussian mixture models (GMMs) and elliptical basis function networks (EBFNs) as speaker models were conducted. The original training algorithm of PDBNNs was also modified to make PDBNNs appropriate for speaker verification. Results show that the equal error rate obtained by PDBNNs and GMMs is less than that of EBFNs (0.33% vs. 0.48%), suggesting that GMM- and PDBNN-based speaker models outperform the EBFN ones. This work also finds that the globally supervised learning of PDBNNs is able to find decision thresholds that not only maintain the false acceptance rates to a low level but also reduce their variation, whereas the ad-hoc threshold-determination approach used by the EBFNs and GMMs causes a large variation in the error rates. This property makes the performance of PDBNN-based systems more predictable.

关键词： Probabilistic neural networks speaker verification Gaussian mixture models decision thresholds reinforced learning

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