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arXiv 2024年

作者： Martínez-García, María Villacrés, Grace Mitchell, David Olmos, Pablo M. Department of Signal Theory and Communications Universidad Carlos III de Madrid Spain Department of Signal Processing and Communications Universidad Rey Juan Carlos Spain Klipsch School of Electrical and Computer Engineering New Mexico State University United States

Despite significant advancements in deep probabilistic models, learning low-dimensional discrete latent representations remains a challenging task. In this paper, we introduce a novel method that enhances variational inference in discrete latent variable models by leveraging Error-Correcting Codes (ECCs) to introduce redundancy in the latent representations. This redundancy is then exploited by the variational posterior to yield more accurate estimates, thereby narrowing the variational gap. Inspired by ECCs commonly used in digital communications and data storage, we demonstrate proof-of-concept using a Discrete Variational Autoencoder (DVAE) with binary latent variables and block repetition codes. We further extend this idea to a hierarchical structure based on polar codes, where certain latent bits are more robustly protected. Our method improves generation quality, data reconstruction, and uncertainty calibration compared to the uncoded DVAE, even when trained with tighter bounds such as the Importance Weighted Autoencoder (IWAE) objective. In particular, we demonstrate superior performance on MNIST, FMNIST, CIFAR10, and Tiny ImageNet datasets. The general approach of integrating ECCs into variational inference is compatible with existing techniques to boost variational inference, such as importance sampling or Hamiltonian Monte Carlo. We also outline the key properties ECCs must have to effectively enhance discrete variational inference. Copyright © 2024, The Authors. All rights reserved.

关键词： Importance sampling

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Adaptive IMM Algorithm Based on Variational Inference for Multiple Maneuvering Extended Targets Tracking

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Advances in Astronautics 2025年 1-15页

作者： Wang, Shenghua Li, Renxian Men, Chenkai Cao, Yunhe Yeo, Tat-Soon School of Communications and Information Engineering and School of Artificial Intelligence Xi’an University of Posts and Telecommunications Xi’an China National Lab of Radar Signal Processing Xidian University Xi’an China The Department of Electrical and Computer Engineering National University of Singapore Singapore Singapore

In order to track multiple maneuvering extended targets accurately, an adaptive interacting multiple model algorithm based on variational inference (AIMM-VI) is proposed. An augmented state is constructed to cater for time-varying orientation angle and track realistic shape changes, resulting in better elliptical shape estimation and tracking accuracy. Multiple measurements from multiple extended targets are effectively assigned to corresponding targets through the marginal association probability distribution criterion, and the variational inference is used to accurately estimate the augmented state and shape information, which greatly improves the parameters estimation performance. The residual and likelihood functions are updated in real-time according to the results of variational inference, allowing for the updating of the model probability in real-time. The Markov probability transfer matrix is subsequently adaptively updated by the compression ratio, which makes the algorithm more adaptable to maneuvering target and significantly improves the adaptability and robustness of the algorithm. The final simulation and experiment results show that the proposed algorithm can effectively improve the tracking performance of multiple maneuvering extended targets.

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Recursive Subproduct Codes with Reed-Muller-like Structure

Recursive Subproduct Codes with Reed-Muller-like Structure

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IEEE International Symposium on Information Theory

作者： Aditya Siddheshwar Lakshmi Prasad Natarajan Prasad Krishnan Signal Processing and Communications Research Center International Institute of Information Technology Hyderabad India Department of Electrical Engineering Indian Institute of Technology Hyderabad Sangareddy India

ISBN: (数字)9798350382846

ISBN: (纸本)9798350382853

We study a family of subcodes of the $m-\mathbf{dimensional}$ product code $\mathcal{C}^{\otimes m}$ (‘subproduct codes’) that have a recursive Plotkin-like structure, and which include Reed-Muller (RM) codes and Dual Berman codes as special cases. We denote the codes in this family as $\mathcal{C}^{\otimes[r,m]}$ , where $r\in\{0,1,\ \ldots,\ m\}$ is the ‘order’ of the code. These codes allow a ‘projection’ operation that can be exploited in iterative decoding, viz., the sum of two carefully chosen subvectors of any codeword in $\mathcal{C}^{\otimes[r,m]}$ belongs to $\mathcal{C}^{\otimes[r-1,m-1]}$ . Recursive subproduct codes provide a wider range of rates and block lengths compared to RM codes while possessing several of their structural properties, such as the Plotkin-like design, the projection property, and fast ML decoding of first-order codes. Our simulation results for first-order and second-order codes, that are based on a belief propagation decoder and a local graph search algorithm, show instances of subproduct codes that perform either better than or within 0.5 dB of comparable RM codes and CRC-aided Polar codes.

关键词： Codes Product codes Simulation Decoding Polar codes Iterative decoding Belief propagation

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Overview of Variable Rate Coding in JPEG AI

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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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Joint Precoding and Fronthaul Compression for Cell-Free MIMO with Hybrid Topology

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IEEE Internet of Things Journal 2025年第11期12卷 18125-18136页

作者： Chen, Yijie Xia, Wenchao Zhang, Jun Hou, Xiaoyun Wong, Kai-Kit Zhu, Hongbo The Jiangsu Key Laboratory of Wireless Communications Nanjing University of Posts and Telecommunications China The Engineering Research Center of Health Service System Based on Ubiquitous Wireless Networks Ministry of Education Nanjing University of Posts and Telecommunications Nanjing210003 China The Institute of Signal Processing and Transmission Nanjing University of Posts and Telecommunications Nanjing210003 China The Department of Electronic and Electrical Engineering University College London Torrington Place WC1E 7JE United Kingdom Yonsei Frontier Lab Yonsei University Seoul Korea Republic of

Cell-free multiple-input multiple-output (MIMO) generally uses a star topology for superior communication but faces high costs due to long cables. An economical alternative, the stripe topology, is suitable for specific deployments but cannot meet user demands in densely populated areas due to limited fronthaul capacity. To address these limitations, we propose a hybrid network structure combining stripe and star topologies, ensuring system performance while reducing deployment costs. In such a network, joint precoding and fronthaul compression is considered to maximize system sum-rate and an alternating optimization (AO) algorithm is proposed. However, the AO algorithm involves an iterative process and complex matrix calculations, making it unsuitable for practical applications. To deal with this issue, we propose a low-complexity iterative gradient descent (IGD) algorithm with simple matrix operations. To further reduce online computational complexity, we propose a novel deep unfolding neural network (DUNN) scheme, which is interpretable and scalable, based on the IGD algorithm. Simulation results show that the hybrid topology significantly improves system capacity compared to the stripe-only topology. Additionally, the DUNN achieves a tradeoff between the achievable sum-rate performance and the corresponding computational complexity. © 2014 IEEE.

关键词： Iterative methods

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Image Segmentation for Improved Lossless Screen Content Compression

Image Segmentation for Improved Lossless Screen Content Comp...

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International Conference on Acoustics, Speech, and signal processing (ICASSP)

作者： Shabhrish Reddy Uddehal Tilo Strutz Hannah Och André Kaup Department of Electrical Engineering and Computer Science Coburg University Coburg Germany Multimedia Communications and Signal Processing Friedrich-Alexander University Erlangen Germany

In recent years, it has been found that screen content images (SCI) can be effectively compressed based on appropriate probability modelling and suitable entropy coding methods such as arithmetic coding. The key objective is determining the best probability distribution for each pixel position. This strategy works particularly well for images with synthetic (textual) content. However, usually screen content images not only consist of synthetic but also pictorial (natural) regions. These images require diverse models of probability distributions to be optimally compressed. One way to achieve this goal is to separate synthetic and natural regions. This paper proposes a segmentation method that identifies natural regions enabling better adaptive treatment. It supplements a compression method known as Soft Context Formation (SCF) and operates as a pre-processing step. If at least one natural segment is found within the SCI, it is split into two subimages (natural and synthetic parts) and the process of modelling and coding is performed separately for both. For SCIs with natural regions, the proposed method achieves a bit-rate reduction of up to 11.6% and 1.52% with respect to HEVC and the previous version of the SCF.

关键词： Image segmentation Adaptation models Image coding signal processing Probability distribution Entropy coding Acoustics

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IMAGE SEGMENTATION FOR IMPROVED LOSSLESS SCREEN CONTENT COMPRESSION

arXiv

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arXiv 2023年

作者： Uddehal, Shabhrish Reddy Strutz, Tilo Och, Hannah Kaup, André Department of Electrical Engineering and Computer Science Coburg University Coburg Germany Multimedia Communications and Signal Processing Friedrich-Alexander University Erlangen Germany

In recent years, it has been found that screen content images (SCI) can be effectively compressed based on appropriate probability modelling and suitable entropy coding methods such as arithmetic coding. The key objective is determining the best probability distribution for each pixel position. This strategy works particularly well for images with synthetic (textual) content. However, usually screen content images not only consist of synthetic but also pictorial (natural) regions. These images require diverse models of probability distributions to be optimally compressed. One way to achieve this goal is to separate synthetic and natural regions. This paper proposes a segmentation method that identifies natural regions enabling better adaptive treatment. It supplements a compression method known as Soft Context Formation (SCF) and operates as a pre-processing step. If at least one natural segment is found within the SCI, it is split into two sub-images (natural and synthetic parts) and the process of modelling and coding is performed separately for both. For SCIs with natural regions, the proposed method achieves a bit-rate reduction of up to 11.6% and 1.52% with respect to HEVC and the previous version of the SCF. Copyright © 2023, The Authors. All rights reserved.

关键词： Probability distributions

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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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Recursive Subproduct Codes with Reed-Muller-like Structure

arXiv

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arXiv 2024年

作者： Siddheshwar, Aditya Natarajan, Lakshmi Prasad Krishnan, Prasad The Signal Processing and Communications Research Center International Institute of Information Technology Hyderabad500032 India The Department of Electrical Engineering Indian Institute of Technology Hyderabad Sangareddy502284 India

We study a family of subcodes of the m-dimensional product code C⊗m (‘subproduct codes’) that have a recursive Plotkin-like structure, and which include Reed-Muller (RM) codes and Dual Berman codes as special cases. We denote the codes in this family as C⊗[r,m], where 0 ≤ r ≤ m is the ‘order’ of the code. These codes allow a ‘projection’ operation that can be exploited in iterative decoding, viz., the sum of two carefully chosen subvectors of any codeword in C⊗[r,m] belongs to C⊗[r−1,m−1]. Recursive subproduct codes provide a wide range of rates and block lengths compared to RM codes while possessing several of their structural properties, such as the Plotkin-like design, the projection property, and fast ML decoding of first-order codes. Our simulation results for first-order and second-order codes, that are based on a belief propagation decoder and a local graph search algorithm, show instances of subproduct codes that perform either better than or within 0.5 dB of comparable RM codes and CRC-aided Polar codes. Copyright © 2024, The Authors. All rights reserved.

关键词： Iterative decoding

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Multi-Task Sparse signal Recovery with Dirichlet Process Priors Based on Expectation Propagation Technique 7

Multi-Task Sparse Signal Recovery with Dirichlet Process Pri...

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7th International Conference on signal and Image processing, ICSIP 2022

作者： Fu, Yin Wu, Qisong Zhang, Yimin D. Amin, Moeness G. Southeast University Key Laboratory of Underwater Acoustic Signal Processing of Ministry of Education Nanjing210096 China Temple University Department of Electrical and Computer Engineering PhiladelphiaPA19122 United States Villanova University Center for Advanced Communications VillanovaPA19085 United States

ISBN: (数字)9781665495639

ISBN: (纸本)9781665495639

Exploiting the shared information among tasks to significantly improve the sparse reconstruction performance lays the essence of multi-task compressive sensing. In this paper, a novel generative model of multi-task compressive sensing with Dirichlet process (DP) priors is proposed and the sharing mechanisms among tasks are revealed, yielding a principled means of inferring the clusters as well as performing compressive sensing inversion simultaneously. The spike-and-slab priors are first used to model the group sparsity among tasks within the identical cluster, and the DP priors are then introduced to automatically perform clustering for the tasks. The expectation propagation method is finally carried out to take the inference for posterior distribution approximation. The superiority of the proposed method over state-of-the-art algorithms is demonstrated by using experimental results on both numerical data and real data sets. © 2022 IEEE.

关键词： Compressed sensing

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