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学者姓名：陈炜玲
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Abstract ：
Sonar technology has been widely used in underwater surface mapping and remote object detection for its light-independent characteristics. Recently, the booming of artificial intelligence further surges sonar image (SI) processing and understanding techniques. However, the intricate marine environments and diverse nonlinear postprocessing operations may degrade the quality of SIs, impeding accurate interpretation of underwater information. Efficient image quality assessment (IQA) methods are crucial for quality monitoring in sonar imaging and processing. Existing IQA methods overlook the unique characteristics of SIs or focus solely on typical distortions in specific scenarios, which limits their generalization capability. In this article, we propose a unified sonar IQA method, which overcomes the challenges posed by diverse distortions. Though degradation conditions are changeable, ideal SIs consistently require certain properties that must be task-centered and exhibit attribute consistency. We derive a comprehensive set of quality attributes from both the task background and visual content of SIs. These attribute features are represented in just ten dimensions and ultimately mapped to the quality score. To validate the effectiveness of our method, we construct the first comprehensive SI dataset. Experimental results demonstrate the superior performance and robustness of the proposed method.
Keyword ：
Attribute consistency Attribute consistency Degradation Degradation Distortion Distortion Image quality Image quality image quality assessment (IQA) image quality assessment (IQA) Imaging Imaging Noise Noise Nonlinear distortion Nonlinear distortion no-reference (NR) no-reference (NR) Quality assessment Quality assessment Silicon Silicon Sonar Sonar sonar imaging and processing sonar imaging and processing Sonar measurements Sonar measurements
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| GB/T 7714 | Cai, Boqin , Chen, Weiling , Zhang, Jianghe et al. Unified No-Reference Quality Assessment for Sonar Imaging and Processing [J]. | IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING , 2025 , 63 . |
| MLA | Cai, Boqin et al. "Unified No-Reference Quality Assessment for Sonar Imaging and Processing" . | IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING 63 (2025) . |
| APA | Cai, Boqin , Chen, Weiling , Zhang, Jianghe , Junejo, Naveed Ur Rehman , Zhao, Tiesong . Unified No-Reference Quality Assessment for Sonar Imaging and Processing . | IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING , 2025 , 63 . |
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Unlike vanilla long-tailed recognition trains on imbalanced data but assumes a uniform test class distribution, test-agnostic long-tailed recognition aims to handle arbitrary test class distributions. Existing methods require prior knowledge of test sets for post-adjustment through multi-stage training, resulting in static decisions at the dataset-level. This pipeline overlooks instance diversity and is impractical in real situations. In this work, we introduce Prototype Alignment with Dedicated Experts (PADE), a one-stage framework for test-agnostic long-tailed recognition. PADE tackles unknown test distributions at the instance-level, without depending on test priors. It reformulates the task as a domain detection problem, dynamically adjusting the model for each instance. PADE comprises three main strategies: 1) parameter customization strategy for multi-experts skilled at different categories; 2) normalized target knowledge distillation for mutual guidance among experts while maintaining diversity; 3) re-balanced compactness learning with momentum prototypes, promoting instance alignment with the corresponding class centroid. We evaluate PADE on various long-tailed recognition benchmarks with diverse test distributions. The results verify its effectiveness in both vanilla and test-agnostic long-tailed recognition.
Keyword ：
Long-tailed classification Long-tailed classification prototypical learning prototypical learning test-agnostic recognition test-agnostic recognition
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| GB/T 7714 | Guo, Chen , Chen, Weiling , Huang, Aiping et al. Prototype Alignment With Dedicated Experts for Test-Agnostic Long-Tailed Recognition [J]. | IEEE TRANSACTIONS ON MULTIMEDIA , 2025 , 27 : 455-465 . |
| MLA | Guo, Chen et al. "Prototype Alignment With Dedicated Experts for Test-Agnostic Long-Tailed Recognition" . | IEEE TRANSACTIONS ON MULTIMEDIA 27 (2025) : 455-465 . |
| APA | Guo, Chen , Chen, Weiling , Huang, Aiping , Zhao, Tiesong . Prototype Alignment With Dedicated Experts for Test-Agnostic Long-Tailed Recognition . | IEEE TRANSACTIONS ON MULTIMEDIA , 2025 , 27 , 455-465 . |
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The recent advances in multimedia technology have significantly expanded the range of audio-visual applications. The continuous enhancement of display quality has led to the emergence of new attributes in video, such as enhanced visual immersion and widespread availability. Within media content, the video signals are presented in various formats including stereoscopic/3D, panoramic/360 degrees degrees and holographic images. The signals are also combined with other sensory elements, such as audio, tactile, and olfactory cues, creating a comprehensive multi-sensory experience for the user. The development of both qualitative and quantitative Quality of Experience (QoE) metrics is crucial for enhancing the subjective experience in immersive scenarios, providing valuable guidelines for system enhancement. In this paper, we review the most recent achievements in QoE assessment for immersive scenarios, summarize the current challenges related to QoE issues, and present outlooks of QoE applications in these scenarios. The aim of our overview is to offer a valuable reference for researchers in the domain of multimedia delivery.
Keyword ：
Immersive video Immersive video MULtiple SEnsorial MEDIA (MULSEMEDIA) MULtiple SEnsorial MEDIA (MULSEMEDIA) Quality of Experience (QoE) Quality of Experience (QoE) Video delivery Video delivery Video transmission Video transmission
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| GB/T 7714 | Chen, Weiling , Lan, Fengquan , Wei, Hongan et al. A comprehensive review of quality of experience for emerging video services [J]. | SIGNAL PROCESSING-IMAGE COMMUNICATION , 2024 , 128 . |
| MLA | Chen, Weiling et al. "A comprehensive review of quality of experience for emerging video services" . | SIGNAL PROCESSING-IMAGE COMMUNICATION 128 (2024) . |
| APA | Chen, Weiling , Lan, Fengquan , Wei, Hongan , Zhao, Tiesong , Liu, Wei , Xu, Yiwen . A comprehensive review of quality of experience for emerging video services . | SIGNAL PROCESSING-IMAGE COMMUNICATION , 2024 , 128 . |
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在"中国制造2025"的国家需求及福建省海西地方经济和产业升级需求的背景下,传统的信号与信息处理专业的培养方式对未来所需的人才品质存在不适应性.通过分析信号与信息处理专业教学体系现状,以福州大学为例,研究人工智能时代的信号专业教育教学改革机制,分别从学位点建设、课程建设、培养方案、培养目标、课程体系等方面探讨了教学改革机制,从而为高等院校培养信号与信息处理方向的综合型创新人才提供参考.
Keyword ：
5G 5G 人工智能 人工智能 信号与信息处理专业 信号与信息处理专业 教学改革 教学改革 课程思政 课程思政
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| GB/T 7714 | 陈炜玲 , 林丽群 , 赵铁松 . "5G+人工智能"时代的教学新挑战 [J]. | 教育教学论坛 , 2024 , (40) : 42-46 . |
| MLA | 陈炜玲 et al. ""5G+人工智能"时代的教学新挑战" . | 教育教学论坛 40 (2024) : 42-46 . |
| APA | 陈炜玲 , 林丽群 , 赵铁松 . "5G+人工智能"时代的教学新挑战 . | 教育教学论坛 , 2024 , (40) , 42-46 . |
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Face Super-Resolution (FSR) plays a crucial role in enhancing low-resolution face images, which is essential for various face-related tasks. However, FSR may alter individuals’ identities or introduce artifacts that affect recognizability. This problem has not been well assessed by existing Image Quality Assessment (IQA) methods. In this paper, we present both subjective and objective evaluations for FSR-IQA, resulting in a benchmark dataset and a reduced reference quality metrics, respectively. First, we incorporate a novel criterion of identity preservation and recognizability to develop our Face Super-resolution Quality Dataset (FSQD). Second, we analyze the correlation between identity preservation and recognizability, and investigate effective feature extractions for both of them. Third, we propose a training-free IQA framework called Face Identity and Recognizability Evaluation of Super-resolution (FIRES). Experimental results using FSQD demonstrate that FIRES achieves competitive performance. IEEE
Keyword ：
Biometrics Biometrics Face recognition Face recognition face super-resolution face super-resolution Feature extraction Feature extraction identity preservation identity preservation Image quality Image quality Image recognition Image recognition Image reconstruction Image reconstruction Measurement Measurement quality assessment quality assessment recognizability recognizability Superresolution Superresolution
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| GB/T 7714 | Chen, W. , Lin, W. , Xu, X. et al. Face Super-Resolution Quality Assessment Based On Identity and Recognizability [J]. | IEEE Transactions on Biometrics, Behavior, and Identity Science , 2024 , 6 (3) : 1-1 . |
| MLA | Chen, W. et al. "Face Super-Resolution Quality Assessment Based On Identity and Recognizability" . | IEEE Transactions on Biometrics, Behavior, and Identity Science 6 . 3 (2024) : 1-1 . |
| APA | Chen, W. , Lin, W. , Xu, X. , Lin, L. , Zhao, T. . Face Super-Resolution Quality Assessment Based On Identity and Recognizability . | IEEE Transactions on Biometrics, Behavior, and Identity Science , 2024 , 6 (3) , 1-1 . |
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Underwater images serve as crucial mediums for conveying marine information. Nevertheless, due to the inherent complexity of the underwater environment, underwater images often suffer from various quality degradation phenomena such as color deviation, low contrast, and non-uniform illumination. These degraded underwater images fail to meet the requirements of underwater computer vision applications. Consequently, effective quality optimization of underwater images is of paramount research and analytical value. Based on whether they rely on underwater physical imaging models, underwater image quality optimization techniques can be categorized into underwater image enhancement and underwater image restoration methods. This paper provides a comprehensive review of underwater image enhancement and restoration algorithms, accompanied by a brief introduction to underwater imaging model. Then, we systematically analyze publicly available underwater image datasets and commonly-used quality assessment methodologies. Furthermore, extensive experimental comparisons are carried out to assess the performance of underwater image optimization algorithms and their practical impact on high-level vision tasks. Finally, the challenges and future development trends in this field are discussed. We hope that the efforts made in this paper will provide valuable references for future research and contribute to the innovative advancement of underwater image optimization.
Keyword ：
Image quality assessment Image quality assessment Underwater image datasets Underwater image datasets Underwater image enhancement Underwater image enhancement Underwater image restoration Underwater image restoration
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| GB/T 7714 | Wang, Mingjie , Zhang, Keke , Wei, Hongan et al. Underwater image quality optimization: Researches, challenges, and future trends [J]. | IMAGE AND VISION COMPUTING , 2024 , 146 . |
| MLA | Wang, Mingjie et al. "Underwater image quality optimization: Researches, challenges, and future trends" . | IMAGE AND VISION COMPUTING 146 (2024) . |
| APA | Wang, Mingjie , Zhang, Keke , Wei, Hongan , Chen, Weiling , Zhao, Tiesong . Underwater image quality optimization: Researches, challenges, and future trends . | IMAGE AND VISION COMPUTING , 2024 , 146 . |
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The limited bandwidth of underwater acoustic channels poses a challenge to the efficiency of multimedia information transmission. To improve efficiency, the system aims to transmit less data while maintaining image utility at the receiving end. Although assessing utility within compressed information is essential, the current methods exhibit limitations in addressing utility-driven quality assessment. Therefore, this letter built a Utility-oriented compacted Image Quality Dataset (UCIQD) that contains utility qualities of reference images and their corresponding compcated information at different levels. The utility score is derived from the average confidence of various object detection models. Then, based on UCIQD, we introduce a Distillation-based Compacted Information Quality assessment metric (DCIQ) for utility-oriented quality evaluation in the context of underwater machine vision. In DCIQ, utility features of compacted information are acquired through transfer learning and mapped using a Transformer. Besides, we propose a utility-oriented cross-model feature fusion mechanism to address different detection algorithm preferences. After that, a utility-oriented feature quality measure assesses compacted feature utility. Finally, we utilize distillation to compress the model by reducing its parameters by 55%. Experiment results effectively demonstrate that our proposed DCIQ can predict utility-oriented quality within compressed underwater information.
Keyword ：
Compacted underwater information Compacted underwater information distillation distillation utility-oriented quality assessment utility-oriented quality assessment
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| GB/T 7714 | Liao, Honggang , Jiang, Nanfeng , Chen, Weiling et al. Distillation-Based Utility Assessment for Compacted Underwater Information [J]. | IEEE SIGNAL PROCESSING LETTERS , 2024 , 31 : 481-485 . |
| MLA | Liao, Honggang et al. "Distillation-Based Utility Assessment for Compacted Underwater Information" . | IEEE SIGNAL PROCESSING LETTERS 31 (2024) : 481-485 . |
| APA | Liao, Honggang , Jiang, Nanfeng , Chen, Weiling , Wei, Hongan , Zhao, Tiesong . Distillation-Based Utility Assessment for Compacted Underwater Information . | IEEE SIGNAL PROCESSING LETTERS , 2024 , 31 , 481-485 . |
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Smart oceanic exploration has greatly benefitted from AI-driven underwater image and video processing. However, the volume of underwater video content is subject to narrow-band and time-varying underwater acoustic channels. How to support high-utility video transmission at such a limited capacity is still an open issue. In this article, we propose a Flexible Underwater Video Codec (FUVC) with separate designs for targets-of-interest regions and backgrounds. The encoder locates all targets of interest, compresses their corresponding regions with x.265, and, if bandwidth allows, compresses the background with a lower bitrate. The decoder reconstructs both streams, identifies clean targets of interest, and fuses them with the background via a mask detection and background recovery (MDBR) network. When the background stream is unavailable, the decoder adapts all targets of interest to a virtual background via Poisson blending. Experimental results show that FUVC outperforms other codecs with a lower bitrate at the same quality. It also supports a flexible codec for underwater acoustic channels. The database and the source code are available at https://github.com/z21110008/FUVC.
Keyword ：
Ocean exploration Ocean exploration smart oceans smart oceans underwater image processing underwater image processing video coding video coding video compression video compression
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| GB/T 7714 | Zheng, Yannan , Luo, Jiawei , Chen, Weiling et al. FUVC: A Flexible Codec for Underwater Video Transmission [J]. | IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING , 2024 , 62 : 18-18 . |
| MLA | Zheng, Yannan et al. "FUVC: A Flexible Codec for Underwater Video Transmission" . | IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING 62 (2024) : 18-18 . |
| APA | Zheng, Yannan , Luo, Jiawei , Chen, Weiling , Li, Zuoyong , Zhao, Tiesong . FUVC: A Flexible Codec for Underwater Video Transmission . | IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING , 2024 , 62 , 18-18 . |
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Recently, many compression algorithms are applied to decrease the cost of video storage and transmission. This will introduce undesirable artifacts, which severely degrade visual quality. Therefore, Video Compression Artifacts Removal (VCAR) aims at reconstructing a high-quality video from its corrupted version of compression. Generally, this task is considered as a vision-related instead of media-related problem. In vision-related research, the visual quality has been significantly improved while the computational complexity and bitrate issues are less considered. In this work, we review the performance constraints of video coding and transfer to evaluate the VCAR outputs. Based on the analyses, we propose a Spatial-Temporal Attention-Guided Enhancement Network (STAGE-Net). First, we employ dynamic filter processing, instead of conventional optical flow method, to reduce the computational cost of VCAR. Second, we introduce self-attention mechanism to design Sequential Residual Attention Blocks (SRABs) to improve visual quality of enhanced video frames with bitrate constraints. Both quantitative and qualitative experimental results have demonstrated the superiority of our proposed method, which achieves high visual qualities and low computational costs.
Keyword ：
Bit rate Bit rate Computational complexity Computational complexity Image coding Image coding Task analysis Task analysis Video coding Video coding Video compression Video compression video compression artifacts removal (VCAR) video compression artifacts removal (VCAR) video enhancement video enhancement video quality video quality Visualization Visualization
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| GB/T 7714 | Jiang, Nanfeng , Chen, Weiling , Lin, Jielian et al. Video Compression Artifacts Removal With Spatial-Temporal Attention-Guided Enhancement [J]. | IEEE TRANSACTIONS ON MULTIMEDIA , 2024 , 26 : 5657-5669 . |
| MLA | Jiang, Nanfeng et al. "Video Compression Artifacts Removal With Spatial-Temporal Attention-Guided Enhancement" . | IEEE TRANSACTIONS ON MULTIMEDIA 26 (2024) : 5657-5669 . |
| APA | Jiang, Nanfeng , Chen, Weiling , Lin, Jielian , Zhao, Tiesong , Lin, Chia-Wen . Video Compression Artifacts Removal With Spatial-Temporal Attention-Guided Enhancement . | IEEE TRANSACTIONS ON MULTIMEDIA , 2024 , 26 , 5657-5669 . |
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The Just Noticeable Difference (JND) model aims to identify perceptual redundancies in images by simulating the perception of the Human Visual System (HVS). Exploring the JND of sonar images is important for the study of their visual properties and related applications. However, there is still room for improvement in performance of existing JND models designed for Natural Scene Images (NSIs), and the characteristics of sonar images are not sufficiently considered by them. On the other hand, there are significant challenges in constructing a densely labeled pixel-level JND dataset. To tackle these issues, we proposed a pixel-level JND model based on inexact supervised learning. A perceptually lossy/lossless predictor was first pre-trained on a coarsegrained picture-level JND dataset. This predictor can guide the unsupervised generator to produce an image that is perceptually lossless compared to the original image. Then we designed a loss function to ensure that the generated image is perceptually lossless and maximally different from the original image. Experimental results show that our model outperforms current models.
Keyword ：
Inexact Supervised Learning Inexact Supervised Learning Just Noticeable Difference (JND) Just Noticeable Difference (JND) Sonar Images Sonar Images
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| GB/T 7714 | Feng, Qianxue , Wang, Mingjie , Chen, Weiling et al. Pixel-Level Sonar Image JND Based on Inexact Supervised Learning [J]. | PATTERN RECOGNITION AND COMPUTER VISION, PRCV 2023, PT XI , 2024 , 14435 : 469-481 . |
| MLA | Feng, Qianxue et al. "Pixel-Level Sonar Image JND Based on Inexact Supervised Learning" . | PATTERN RECOGNITION AND COMPUTER VISION, PRCV 2023, PT XI 14435 (2024) : 469-481 . |
| APA | Feng, Qianxue , Wang, Mingjie , Chen, Weiling , Zhao, Tiesong , Zhu, Yi . Pixel-Level Sonar Image JND Based on Inexact Supervised Learning . | PATTERN RECOGNITION AND COMPUTER VISION, PRCV 2023, PT XI , 2024 , 14435 , 469-481 . |
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