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Scene-Adaptive Reference Selection and Inter Prediction for Gaming Video Coding 5

Scene-Adaptive Reference Selection and Inter Prediction for ...

引用

5th International Seminar on Artificial Intelligence, Networking and Information Technology, AINIT 2024

作者： Lin, Chengda Lin, Simin Fang, Ying Yi, Shiqi Wei, Hongan Xu, Yiwen Fuzhou University Fujian Key Lab For Intelligent Processing and Wireless Transmission of Media Information Fuzhou350108 China

ISBN: (纸本)9798350385557

With the burgeoning growth of the e-sports industry, there has been a rapid proliferation of gaming videos on online platforms. Simultaneously, this surge undoubtedly presents significant challenges to the encoding of game videos. However, the recurring characteristic of gaming videos is not efficiently studied in the current standardization, such as Versatile Video Coding (VVC). Based on these observations, our general framework, utilizing the Structural SIMilarity index metric (SSIM), Hash SIMilarity index metric (HSIM), and Hash Matrix SIMilarity index metric (HMSIM), identifies recurring video clips and adjusts the reference frame of the first frame. Additionally, we analyze recurring patterns in different resolutions and types, proposing our Scene Adaptation (SA) optimization algorithm, which integrates SSIM, HSIM, and HMSIM to adapt to various resolutions and scenes. Experimental results show that the proposed approach can achieve a -4.71 % Bjontegaard Delta Bit Rate (BDBR) and 1.00% Time Save (TS), outperforming the benchmark. © 2024 IEEE.

关键词： Video signal processing

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A Correction-Based Dynamic Enhancement Framework Towards Underwater Detection 9th

A Correction-Based Dynamic Enhancement Framework Towards Und...

引用

9th International Forum on Digital Multimedia Communication, IFTC 2022

作者： Qiu, Yanling Feng, Qianxue Cai, Boqin Wei, Hongan Chen, Weiling Fujian Key Lab for Intelligent Processing and Wireless Transmission of Media Information Fuzhou University Fuzhou350108 China

ISBN: (纸本)9789819908554

To assist underwater object detection for better performance, image enhancement technology is often used as a pre-processing step. However, most of the existing enhancement methods tend to pursue the visual quality of an image, instead of providing effective help for detection tasks. In fact, image enhancement algorithms should be optimized with the goal of utility improvement. In this paper, to adapt to the underwater detection tasks, we proposed a lightweight dynamic enhancement algorithm using a contribution dictionary to guide low-level corrections. Dynamic solutions are designed to capture differences in detection preferences. In addition, it can also balance the inconsistency between the contribution of correction operations and their time complexity. Experimental results in real underwater object detection tasks show the superiority of our proposed method in both generalization and real-time performance. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Object detection

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Underwater Image Enhancement and Restoration Techniques: A Comprehensive Review, Challenges, and Future Trends 18th

Underwater Image Enhancement and Restoration Techniques: A C...

引用

18th Chinese Conference on Image and Graphics Technology and Application Conference, IGTA 2023

作者： Wang, Mingjie Lan, Fengquan Su, Zezhao Chen, Weiling Fujian Key Lab for Intelligent Processing and Wireless Transmission of Media Information Fuzhou University Fuzhou350108 China

ISBN: (纸本)9789819975488

Underwater optical images serve as crucial carriers and representations of ocean information. They play a vital role in the field of marine exploration. However, the quality of images captured by underwater cameras often falls short of the expected standards due to the complex underwater environment. This limitation significantly hampers the application and advancement of intelligent underwater image processing systems. Consequently, underwater image enhancement and restoration have been attracting extensive research efforts. In this paper, we review the degradation mechanisms and imaging models of underwater images, and summarize the challenges associated with underwater image enhancement and restoration. Meanwhile, we provide a comprehensive overview of the research progress in underwater optical image enhancement and restoration, and introduces the publicly available underwater image datasets and commonly-used quality evaluation metrics. Through extensive and systematic experiments, the superiority and limitations of underwater image enhancement and restoration methods are further explored. Finally, this review discusses the existing issues in this field and prospects future research directions. It is hoped that this paper will provide valuable references for future studies and contribute to the advancement of research in this domain. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Image reconstruction

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M-RAT: a Multi-grained Retrieval Augmentation Transformer for Image Captioning 17th

M-RAT: a Multi-grained Retrieval Augmentation Transformer f...

引用

17th Asian Conference on Computer Vision, ACCV 2024

作者： Song, Jiayan Pan, Renjie Zhou, Jun Yang, Hua Institute of Image Communication and Network Engineering Shanghai Jiao Tong University Shanghai200240 China Shanghai Key Lab of Digital Media Processing and Transmission Shanghai200240 China

ISBN: (纸本)9789819609079

Current encoder-decoder methods for image captioning mai-nly consist of an object detection module (two-stage), or rely on big models with large-scale datasets to improve the effectiveness, which leads to increasing computation costs and cannot introduce new external knowledge. In this paper, we propose a novel end-to-end method Multi-grained Retrieval Augmentation Transformer (M-RAT) that innovatively fuses retrieved text derived from a changeable datastore with input visual feature through a Multi-modal Aligned Encoder, and introduce a specialized attention mechanism, Multi-MSA, to exploit both local and global interactions for delicate fine-grained details. Additionally, we enhance the decoder generation ability by employing low-level and high-level fused embeddings. Experiments demonstrate that M-RAT achieves comparable performance to state-of-the-art baselines with remarkable accuracy and details, as well as showing excellent domain adaptability for novel objects. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Decoding

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Personalized Federated Learning with Neighbor Aggregation for Traffic Flow Prediction 9

Personalized Federated Learning with Neighbor Aggregation fo...

引用

9th International Conference on Computer and Communications, ICCC 2023

作者： Guo, Junxin Feng, Xinxin Zheng, Haifeng Fuzhou University Fujian Key Lab for Intelligent Processing and Wireless Transmission of Media Information College of Physics and Information Engineering Fuzhou China

ISBN: (纸本)9798350317251

As an important part of Intelligent Transportation System (ITS), traffic flow prediction has attracted more and more attention from academia and industry. However, since traffic flow data are distributed among different organizations, traditional centralized learning methods are no longer applicable, thus federated learning has begun to play an important role in traffic flow prediction tasks. In this paper, we design an improved traffic flow prediction model based on federated learning. At the same time, we consider the interaction of topology and feature space between traffic data owned by different organizations, and propose a method named Federated Neighbor Aggregation (FedNe). The server update the model parameters with both the physical connectivity and similarity relationships between the organizations, and finally generate a personalized model for each organization. Experimental results on real-world dataset show that our proposed method has the lowest average MAE and average RMSE. Finally, based on our method, when new clients participate, they can also quickly obtain their personalized models. © 2023 IEEE.

关键词： Forecasting

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Target Parameter Estimation with Deep Unfolding Networks for MIMO-OFDM Based Integrated Sensing and Communication Systems 15

Target Parameter Estimation with Deep Unfolding Networks for...

引用

15th IEEE International Conference on Wireless Communications and Signal processing, WCSP 2023

作者： Lin, Jingcai Zheng, Haifeng Feng, Xinxin Fuzhou University Fujian Key Lab for Intelligent Processing and Wireless Transmission of Media Information College of Physics and Information Engineering Fuzhou350108 China

ISBN: (纸本)9798350324662

The multiple-input multiple-output orthogonal frequency division multiplexing (MIMO-OFDM) technology shows great potential in integrated sensing and communication (ISAC) systems. To address the challenges posed by the high computational complexity and low estimation accuracy of traditional target parameter estimation methods in a MIMO-OFDM based ISAC system, this paper introduces a compressed sensing based approach for target parameter estimation. We first derive the channel model within the MIMO-OFDM system, and then reformulate the problem of target parameter estimation as a least absolute shrinkage and selection operator (LASSO) problem. Furthermore, we propose a model-driven network, termed FISTA-Net, which deeply unfolds the fast iterative shrinkage thresholding algorithm (FISTA) into a deep neural network to solve the LASSO problem. Experimental results show that the proposed FISTA-Net algorithm outperforms the existing methods in terms of estimation accuracy and computation efficiency for MIMO-OFDM based target parameter estimation. © 2023 IEEE.

关键词： compressed sensing deep unfolding FISTA MIMO-OFDM parameter estimation

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A Region of Interest Extraction Algorithm for Underwater Surveillance Video 11

A Region of Interest Extraction Algorithm for Underwater Sur...

引用

2023 IEEE 11th International Conference on Information, Communication and Networks, ICICN 2023

作者： He, Nian Huang, Jingquan Zhao, Tiesong Xu, Yiwen Zhicheng College Fuzhou University Fuzhou China Fuzhou University Fujian Key Lab for Intelligent Processing and Wireless Transmission of Media Information Fuzhou China

ISBN: (纸本)9798350314014

Underwater Surveillance Video (USV) plays important role in the research and development of marine resources. The harsh underwater environment means that USV can only be transmitted over hydroacoustic channel with narrow bandwidth. Region of Interest (ROI)-based rate control algorithm, which automatically adjusts the code rate to accommodate bandwidth changes based on user's interest. So the ROI Extraction Algorithm (ROI-EA) is a key process. But the existing ROI-EA is mainly aimed at conventional video. It does not take into account the characteristics of USV. Thus, there is still room for improvement in its performance. Therefore, an ROI-EA for USV is proposed in this paper. The characteristics of USV was explored first. Then a underwater image enhancement algorithm based on improved gamma-corrected was proposed. Finally, the proposed ROI-EA, according to the characteristics of USV, was demonstrated. We also compared the performance of our method with existing ROI-EAs. The results show the superiority of our algorithm. © 2023 IEEE.

关键词： Image enhancement

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引用

48th IEEE International Conference on Acoustics, Speech and Signal processing, ICASSP 2023

作者： Yang, Jinhai Yang, Hua Shanghai Jiao Tong University Institution of Image Communication and Network Engineering China Shanghai Key Lab of Digital Media Processing and Transmission Shanghai China

ISBN: (纸本)9781728163277

Few-shot learning aims for rapid adaptation with few samples. Recently, cross-granularity few-shot learning has emerged as a promising research area, where models observe coarse labels but target fine-grained recognition among novel classes. As coarse supervision tends to eliminate feature discrimination among underlying sub-classes, existing methods commonly utilize self-supervision as a complement to explore intra-class variation. However, current methods suffer from an intrinsic conflict between contrastive learning and coarse supervision. In this paper, we locate the root cause of the intrinsic conflict. Then, we resolve it by exploiting the similarity among augmented views while ignoring the unreasonable constraint between negative pairs. Besides, we decouple contrastive learning and coarse supervision into parallel branches to better regularize the latent space. Albeit simple, our approach consistently outperforms state-of-the-art methods across different benchmarks. © 2023 IEEE.

关键词： cross-granularity transfer Few-shot learning generalization image classification

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Hydrodynamics-Informed Neural Network for Simulating Dense Crowd Motion Patterns 24

Hydrodynamics-Informed Neural Network for Simulating Dense C...

引用

32nd ACM International Conference on Multimedia, MM 2024

作者： Zhou, Yanshan Lai, Pingrui Yu, Jiaqi Xiong, Yingjie Yang, Hua Institute of Image Communication and Network Engineering Shanghai Jiao Tong University Shanghai China Shanghai Key Lab of Digital Media Processing and Transmission Shanghai Jiao Tong University Shanghai China

ISBN: (纸本)9798400706868

With global occurrences of crowd crushes and stampedes, dense crowd simulation has been drawing great attention. In this research, our goal is to simulate dense crowd motions under six classic motion patterns, more specifically, to generate subsequent motions of dense crowds from the given initial states. Since dense crowds share similarities with fluids, such as continuity and fluidity, one common approach for dense crowd simulation is to construct hydrodynamics-based models, which consider dense crowds as fluids, guide crowd motions with Navier-Stokes equations, and conduct dense crowd simulation by solving governing equations. Despite the proposal of these models, dense crowd simulation faces multiple challenges, including the difficulty of directly solving Navier-Stokes equations due to their nonlinear nature, the ignorance of distinctive crowd characteristics which fluids lack, and the gaps in the evaluation and validation of crowd simulation models. To address the above challenges, we build a hydrodynamic model, which captures the crowd physical properties (continuity, fluidity, etc.) with Navier-Stokes equations and reflects the crowd social properties (sociality, personality, etc.) with operators that describe crowd interactions and crowd-environment interactions. To tackle the computational problem, we propose to solve the governing equation based on Navier-Stokes equations using neural networks, and introduce the Hydrodynamics-Informed Neural Network (HINN) which preserves the structure of the governing equation in its network architecture. To facilitate the evaluation, we construct a new dense crowd motion video dataset called Dense Crowd Flow Dataset (DCFD), containing six classic motion patterns (line, curve, circle, cross, cluster and scatter) and 457 video clips, which can serve as the groundtruths for various objective metrics. Numerous experiments are conducted using HINN to simulate dense crowd motions under six motion patterns with video clips fro

关键词： Navier Stokes equations

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L2RT-FIQA: Face Image Quality Assessment via Learning-to-Rank Transformer 9th

L2RT-FIQA: Face Image Quality Assessment via Learning-to-Ra...

引用

9th International Forum on Digital Multimedia Communication, IFTC 2022

作者： Chen, Zehao Yang, Hua Institute of Image Communication and Network Engineering Shanghai Jiao Tong University Shanghai China Shanghai Key Lab of Digital Media Processing and Transmission Shanghai China

ISBN: (纸本)9789819908554

Face recognition (FR) systems are easily constrained by complex environmental situations in the wild. To ensure the accuracy of FR systems, face image quality assessment (FIQA) is applied to reject low-quality face image unsuitable for recognition. Face quality can be defined as the accuracy or confidence of face images being correctly recognized by FR systems, which is desired to be consistent with recognition results. However, current FIQA methods show more or less inconsistency with face recognition due to the following four biases, including implicit constraint, quality labels, regression models, and backbone networks. In order to reduce such biases and enhance the consistency between FR and FIQA, this paper proposes a FIAQ method based on Learning to rank (L2R) algorithm and vision Transformer named L2RT-FIQA. L2RT-FIQA consists of three parts: relative quality labels, L2R framework, and vision Transformer backbone. Specifically, we utilize normalized intra-class and inter-class angular distance to generate relative quality labels;we employ L2R model to focus more on the quality order rather than the absolute quality value;we apply unpretrained vision transformer as our backbone to improve generalization and global information learning. Experimental results show our L2RT-FIQA effectively reduces the aforementioned four kinds of biases and outperforms other state-of-the-art FIQA methods on several challenging benchmarks. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Face recognition

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