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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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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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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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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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Super-Resolution (SR) algorithms aim to enhance the resolutions of images. Massive deep-learning-based SR techniques have emerged in recent years. In such case, a visually appealing output may contain additional details compared with its reference image. Accordingly, fully referenced Image Quality Assessment (IQA) cannot work well; however, reference information remains essential for evaluating the qualities of SR images. This poses a challenge to SR-IQA: How to balance the referenced and no-reference scores for user perception? In this paper, we propose a Perception-driven Similarity-Clarity Tradeoff (PSCT) model for SR-IQA. Specifically, we investigate this problem from both referenced and no-reference perspectives, and design two deep-learning-based modules to obtain referenced and no-reference scores. We present a theoretical analysis based on Human Visual System (HVS) properties on their tradeoff and also calculate adaptive weights for them. Experimental results indicate that our PSCT model is superior to the state-of-the-arts on SR-IQA. In addition, the proposed PSCT model is also capable of evaluating quality scores in other image enhancement scenarios, such as deraining, dehazing and underwater image enhancement. The source code is available at https://github.com/kekezhang112/PSCT. © 1991-2012 IEEE.
Keyword ：
Deep learning Deep learning Demulsification Demulsification Feature extraction Feature extraction Image enhancement Image enhancement Image quality Image quality Job analysis Job analysis Optical resolving power Optical resolving power Quality control Quality control
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| GB/T 7714 | Zhang, Keke , Zhao, Tiesong , Chen, Weiling et al. Perception-Driven Similarity-Clarity Tradeoff for Image Super-Resolution Quality Assessment [J]. | IEEE Transactions on Circuits and Systems for Video Technology , 2024 , 34 (7) : 5897-5907 . |
| MLA | Zhang, Keke et al. "Perception-Driven Similarity-Clarity Tradeoff for Image Super-Resolution Quality Assessment" . | IEEE Transactions on Circuits and Systems for Video Technology 34 . 7 (2024) : 5897-5907 . |
| APA | Zhang, Keke , Zhao, Tiesong , Chen, Weiling , Niu, Yuzhen , Hu, Jinsong , Lin, Weisi . Perception-Driven Similarity-Clarity Tradeoff for Image Super-Resolution Quality Assessment . | IEEE Transactions on Circuits and Systems for Video Technology , 2024 , 34 (7) , 5897-5907 . |
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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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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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Due to the light-independent imaging characteristics, sonar images play a crucial role in fields such as underwater detection and rescue. However, the resolution of sonar images is negatively correlated with the imaging distance. To overcome this limitation, Super-Resolution (SR) techniques have been introduced into sonar image processing. Nevertheless, it is not always guaranteed that SR maintains the utility of the image. Therefore, quantifying the utility of SR reconstructed Sonar Images (SRSIs) can facilitate their optimization and usage. Existing Image Quality Assessment (IQA) methods are inadequate for evaluating SRSIs as they fail to consider both the unique characteristics of sonar images and reconstruction artifacts while meeting task requirements. In this paper, we propose a Perception-and-Cognition-inspired quality Assessment method for Sonar image Super-resolution (PCASS). Our approach incorporates a hierarchical feature fusion-based framework inspired by the cognitive process in the human brain to comprehensively evaluate SRSIs' quality under object recognition tasks. Additionally, we select features at each level considering visual perception characteristics introduced by SR reconstruction artifacts such as texture abundance, contour details, and semantic information to measure image quality accurately. Importantly, our method does not require training data and is suitable for scenarios with limited available images. Experimental results validate its superior performance.
Keyword ：
hierarchical feature fusion hierarchical feature fusion image quality assessment (IQA) image quality assessment (IQA) Sonar image Sonar image super-resolution (SR) super-resolution (SR) task-oriented task-oriented
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| GB/T 7714 | Chen, Weiling , Cai, Boqin , Zheng, Sumei et al. Perception-and-Cognition-Inspired Quality Assessment for Sonar Image Super-Resolution [J]. | IEEE TRANSACTIONS ON MULTIMEDIA , 2024 , 26 : 6398-6410 . |
| MLA | Chen, Weiling et al. "Perception-and-Cognition-Inspired Quality Assessment for Sonar Image Super-Resolution" . | IEEE TRANSACTIONS ON MULTIMEDIA 26 (2024) : 6398-6410 . |
| APA | Chen, Weiling , Cai, Boqin , Zheng, Sumei , Zhao, Tiesong , Gu, Ke . Perception-and-Cognition-Inspired Quality Assessment for Sonar Image Super-Resolution . | IEEE TRANSACTIONS ON MULTIMEDIA , 2024 , 26 , 6398-6410 . |
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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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