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检索条件"任意字段=Visual Communications and Image Processing, VCIP 2015"

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End-to-end Prediction of Streaming Video Quality of Experience: Dataset and Approach

End-to-end Prediction of Streaming Video Quality of Experien...

引用

2024 Conference on visual communications and image processing

作者： Jia, Ziheng Min, Xiongkuo Zhai, Guangtao Shanghai Jiao Tong Univ Inst Image Commun & Network Engn Shanghai Peoples R China

ISBN: (纸本)9798331529543;9798331529550

With the rapid development of video-on-demand (VOD) and real-time streaming video technologies, the accurate objective assessment of streaming video Quality of Experience (QoE) has become a focal point for optimizing streaming-related technologies. However, due to the inherent transmission distortions caused by poor Quality of Service (QoS) conditions in streaming videos, such as intermittent stalling, rebuffering, and drastic changes in video sharpness due to bitrate fluctuations, evaluating streaming video QoE presents numerous challenges. This paper introduces a large and diverse in-the-wild streaming video QoE evaluation dataset - the SJLIVE-1k dataset. This work addresses the limitations of corresponding datasets, which lack in-the-wild video sequences under real network conditions and whose amount of video content is insufficient. Furthermore, we propose an end-to-end objective QoE evaluation strategy that extracts video content and QoS features from the video itself without using any extra information. By implementing self-supervised contrastive learning as the "reminder" to bridge the gap between the different types of features, our approach achieves state-of-the-art results across three datasets. Our proposed dataset will be released to facilitate further research.

关键词： Quality of Experience streaming videos contrastive learning

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Frequency-Aware Hierarchical image Compression for Humans and Machines

Frequency-Aware Hierarchical Image Compression for Humans an...

引用

2024 Conference on visual communications and image processing

作者： Luo, Yue Zhang, Zixiang Kuang, Jinhao Yu, Li Huazhong Univ Sci & Technol Wuhan Peoples R China Huazhong Univ Sci & Technol Shenzhen Res Inst Shenzhen Peoples R China

ISBN: (纸本)9798331529543;9798331529550

To achieve efficient compression for both human vision and machine perception, scalable coding methods have been proposed in recent years. However, existing methods do not fully eliminate the redundancy between features corresponding to different tasks, resulting in suboptimal coding performance. In this paper, we propose a frequency-aware hierarchical image compression framework designed for humans and machines. Specifically, we investigate task relationships from a frequency perspective, utilizing only HF information for machine vision tasks and leveraging both HF and LF features for image reconstruction. Besides, the residual block embedded octave convolution module is designed to enhance the information interaction between HF features and LF features. Additionally, a dual-frequency channel-wise entropy model is applied to reasonably exploit the correlation between different tasks, thereby improving multi-task performance. The experiments show that the proposed method offers -69.3%similar to-75.3% coding gains on machine vision tasks compared to the relevant benchmarks, and -19.1% gains over state-of-the-art scalable image codec in terms of image reconstruction quality.

关键词： scalable image coding image coding for machines learned image compression

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Cross-Device image Saliency Detection: Database and Comparative Analysis

Cross-Device Image Saliency Detection: Database and Comparat...

引用

2024 Conference on visual communications and image processing

作者： Ding, Xiaoying Yue, Guanghui Zhang, Yingxue Zhongnan Univ Econ & Law Sch Informat Engn Wuhan Peoples R China Shenzhen Univ Med Sch Sch Biomed Engn Shenzhen Peoples R China Tianjin Univ Sci & Technol Coll Artificial Intelligence Tianjin Peoples R China

ISBN: (纸本)9798331529543;9798331529550

While saliency detection for images has been extensively studied during the past decades, only a little work explores the influence of different viewing devices (i.e., tablet computer, mobile phone) towards human visual attention behavior. The lack of research in this field hinders the research progress in cross-device image saliency detection. In this paper, we first establish a novel cross-device saliency detection (CDSD) database based on eye-tracking experiments and investigate subjects' visual attention behavior when using different viewing devices. Then, we evaluate several classic saliency detection models using the CDSD database and the evaluation results indicate that the cross-device performance of these models need further improvement. Finally, some meaningful discussions are provided which might enlighten the design of cross-device saliency detection model. The proposed CDSD database will be made publicly available.

关键词： cross-device saliency detecton visual attention eye-tracking

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MSCFormer: Multi-Scale Circular Transformer for image Deblurring

MSCFormer: Multi-Scale Circular Transformer for Image Deblur...

引用

2024 Conference on visual communications and image processing

作者： Wang, Shuai Wang, Han Liu, Renhe Wu, Zhipeng Wei, Bo Liu, Yu Tianjin Univ Sch Microelect Tianjin Peoples R China Univ Tokyo Sch Engn Tokyo Japan

ISBN: (纸本)9798331529543;9798331529550

Currently, with the extensive application of digital cameras in dynamic capturing, implications such as camera jitter, out-of-focus, and target motion induce various types and degrees of image blurring. Deep learning (DL) is a powerful technique that offers data-adaptive recovery without prior characterization of deblurring filter kernels. However, end-to-end networks can still be improved to restore regions with severe localized blurring. Therefore, we propose a multi-scale circular transformer (MSC-Former) employing averaged neighborhood attention (AvgNA) to solve this problem. It computes the local attention of each feature pixel by learning the correlation between the center and the surrounding windowed neighborhood, then produces integrated attention with direct averaging. We employ a multiscale circular strategy (MSCS) to compute attention at different spatial scales to expand the receptive field while maintaining a low parameter count. It uses concentric circular regions with varying radii to define neighborhoods at different scales, which expands the receptive field during attention computation while capturing spatial continuity across larger neighborhoods. Experimental results demonstrate that the proposed method surpasses the recent state-of-the-art deblurring techniques on the benchmark dataset.

关键词： image deblurring averaged neighborhood attention multi-scale circular strategy transformer

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Efficient Bitrate Ladder Construction for Per-Shot Adaptive Encoding

Efficient Bitrate Ladder Construction for Per-Shot Adaptive ...

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2024 Conference on visual communications and image processing

作者： Zhao, Yan Cheng, ZhengXue Lu, Guo Xie, Rong Song, Li Shanghai Jiao Tong Univ Inst Image Commun & Network Engn Shanghai Peoples R China

ISBN: (纸本)9798331529543;9798331529550

HTTP adaptive streaming (HAS) constructs bitrate ladders to deliver videos with the best possible quality under varying network conditions. Though per-shot content adaptive encoding (CAE) largely improves the compression efficiency by constructing the optimal bitrate ladder for each video shot, it suffers from excessive encoding complexity as all the points in the operating space (typically resolution x bitrate) need to be encoded and compared. To address this issue, this paper proposes an efficient bitrate ladder construction method that encodes only a subset of operating points, then uses curve fitting and inter-curve prediction to estimate other points' RD performance. The proposed method enables low-complexity ladder construction even for high-dimension operating spaces that incorporate dimensions like encoding presets. Experiments show that this method can achieve RD performance comparable to the original per-shot CAE with only 42% encoding points. Even when minimizing the encoding points to 3.6% of the original CAE, it achieves 15% BDRate improvements compared to using the fixed bitrate ladder.

关键词： Content-adaptive encoding per-shot encoding bitrate ladder prediction adaptive streaming

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Enhancing Privacy-Utility Tradeoff with Few-Round Strategy in Heterogeneous Federated Learning

Enhancing Privacy-Utility Tradeoff with Few-Round Strategy i...

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2024 Conference on visual communications and image processing

作者： Wei, Qingbin Zhang, Feilong Bai, Yuanchao Zhai, Deming Jiang, Junjun Liu, Xianming Harbin Inst Technol Fac Comp Harbin Peoples R China

ISBN: (纸本)9798331529543;9798331529550

Federated learning inherently provides a certain level of privacy protection, which however is often inadequate in many real-world scenarios. Existing privacy-preserving methods frequently incur unbearable time overheads or result in non-negligible deterioration to model performance, thus suffering from the tradeoff between performance and privacy. In this work, we propose a novel Federated Privacy-Preserving Knowledge Transfer framework, namely FedPPKT, which employs data-free knowledge distillation in a meta-learning manner to rapidly generates pseudo data and performs privacy-preserving knowledge transfer. FedPPKT establishes a protective barrier between the original private data and the federated model, thereby ensuring user privacy. Furthermore, leveraging the few-round strategy of FedPPKT, it has the capability to reduce the number of communication rounds, further mitigating the risk of privacy exposure for user data. With the help of the meta generator, the problem of uneven local label distribution on clients is alleviated, mitigating data heterogeneity and improving model performance. Experiments show that FedPPKT outperforms the state-of-the-art privacy-preserving federated learning methods. Our code is publicly available at https://***/HIT-weiqb/FedPPKT.

关键词： Generative image Compression Learned image Compression

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A Practical Approach to Depth-Aware Augmentation for Neural Radiance Fields

A Practical Approach to Depth-Aware Augmentation for Neural ...

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2024 Conference on visual communications and image processing

作者： Khosroshahi, Hamed Razavi Sancho, Jaime Bonatto, Daniele Fachada, Sarah Bang, Gun Lafruit, Gauthier Juarez, Eduardo Teratani, Mehrdad Univ Libre Bruxelles Lab Image Synth & Anal Brussels Belgium Univ Politecn Madrid Res Ctr Software Technol & Multimedia Syst Madrid Spain Telecommun Res Inst Elect Daejeon South Korea

ISBN: (纸本)9798331529543;9798331529550

Neural Radiance Fields (NeRF) have demonstrated exceptional performance in generating novel views of scenes by learning implicit volumetric representations from calibrated RGB images, without depth information. A major limitation is the need for large training datasets in neural network-based view synthesis frameworks. The challenge of effective data augmentation for view synthesis remains unresolved. NeRF models require extensive scene coverage from multiple views to accurately estimate radiance and density. Insufficient coverage reduces the model's ability to interpolate or extrapolate unseen parts of the scene effectively. In this paper, we propose a novel pipeline to address this data augmentation issue using depth map information. We use depth image-based rendering (DIBR) to overcome the lack of enough views for training NeRF. Experimental results indicate that our approach enhances the quality of rendered images using the NeRF framework, achieving an average peak signal-to-noise ratio (PSNR) increase of 7.2 dB, with a maximum improvement of 12 dB.

关键词： Neural Radiance Fields NeRF View synthesis Data augmentation Depth map

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Color Enhancement for V-PCC Compressed Point Cloud via 2D Attribute Map Optimization

Color Enhancement for V-PCC Compressed Point Cloud via 2D At...

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2024 Conference on visual communications and image processing

作者： Bao, Jingwei Liu, Yu Li, Zeliang Zhu, Shuyuan Yeung, Siu-Kei Au Univ Elect Sci & Technol China Sch Informat & Commun Engn Chengdu Peoples R China Hong Kong Metropolitan Univ Sch Sci & Technol Hong Kong Peoples R China

ISBN: (纸本)9798331529543;9798331529550

Video-based point cloud compression (V-PCC) converts the dynamic point cloud data into video sequences using traditional video codecs for efficient encoding. However, this lossy compression scheme introduces artifacts that degrade the color attributes of the data. This paper introduces a framework designed to enhance the color quality in the V-PCC compressed point clouds. We propose the lightweight de-compression Unet (LDC-Unet), a 2D neural network, to optimize the projection maps generated during V-PCC encoding. The optimized 2D maps will then be back-projected to the 3D space to enhance the corresponding point cloud attributes. Additionally, we introduce a transfer learning strategy and develop a customized natural image dataset for the initial training. The model was then fine-tuned using the projection maps of the compressed point clouds. The whole strategy effectively addresses the scarcity of point cloud training data. Our experiments, conducted on the public 8i voxelized full bodies long sequences (8iVSLF) dataset, demonstrate the effectiveness of our proposed method in improving the color quality.

关键词： Point cloud compression image restoration Transfer learning Point cloud reconstruction

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ACIQA: A Dataset and Method for Assessing the Imaging Quality of Automotive Cameras

ACIQA: A Dataset and Method for Assessing the Imaging Qualit...

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2024 Conference on visual communications and image processing

作者： Huang, Yijie Ni, Haoyang Lu, Fangfang Zhang, Kaiwei Jia, Ziheng Min, Xiongkuo Zhai, Guangtao Shanghai Univ Elect Power Shanghai Peoples R China Shanghai Jiao Tong Univ Inst Image Commun & Network Engn Shanghai Peoples R China

ISBN: (纸本)9798331529543;9798331529550

The imaging quality of automotive cameras is crucial in complex driving environments. Therefore, it is essential to conduct subjective experiments that can realistically reflect drivers' evaluation of the imaging quality of automotive cameras in real traffic scenarios. To accurately assess the imaging quality of automotive cameras, this paper proposes a no-reference quality assessment method with quality scores that are highly consistent with human subjective perception. Initially, this study constructs a new image quality assessment dataset and then obtains the subjective scores of image quality through subjective experiments. The dataset is constructed by using a variety of realistic props to simulate scene elements that might be captured by an automotive camera and are captured using a wide range of cameras with different sensor types, lens focus, and viewing angles, resulting in a dataset of diverse images. The objective quality assessment method proposed in this paper consists of an object detection network and a multi-branch quality evaluation network. The object detection network is responsible for identifying and classifying scene elements, while the multi-branch quality evaluation network performs feature extraction and score regression on various types of elements to effectively evaluate the imaging quality of the automotive cameras. In the experiments, this no-reference quality assessment method is tested on our built dataset, and the results show that the proposed method exhibits the best performance compared with the state-of-the-art image quality assessment methods.

关键词： image quality assessment automotive camera image dataset multi-branch quality evaluation network

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ReLI-QA: A Multidimensional Quality Assessment Dataset for Relighted Human Heads

ReLI-QA: A Multidimensional Quality Assessment Dataset for R...

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2024 Conference on visual communications and image processing

作者： Zhou, Yingjie Zhang, Zicheng Wen, Farong Jia, Jun Min, Xiongkuo Wang, Jia Zhai, Guangtao Shanghai Jiao Tong Univ Inst Image Commun & Network Engn Shanghai Peoples R China PengCheng Lab Shenzhen Peoples R China

ISBN: (纸本)9798331529543;9798331529550

Lighting conditions significantly affect the quality of both real and AI-generated images. Facial images are particularly sensitive to lighting due to their detailed nature and the importance of facial features in conveying identity. Poor lighting can easily obscure these critical details. To address this issue, various portrait relighting methods have been developed to adjust the lighting in improperly exposed images. However, these methods often encounter challenges such as overexposure, underexposure, and detail loss in the relighted portraits. Consequently, there is a need for effective quality assessment and control of relighted human heads (RHHs). In this study, one proposed simple baseline and three typical relighting methods are applied to six selected human head (HH) images, resulting in the creation of a quality assessment dataset named ReLI-QA, which comprises 840 RHHs. A multidimensional subjective quality assessment method based on visual guidance is proposed to accurately evaluate the visual quality of each RHH in the dataset. By analyzing the results of subjective experiments, the quality of RHHs is shown to be affected by multiple factors. Finally, based on ReLI-QA, some typical image quality assessment (IQA) methods are selected for benchmark experiments. The experimental results show the limitations of the existing methods in RHH quality assessment. The dataset and code for this research has been released at https://***/zyj-2000/ReLI-QA.

关键词： image Quality Assessment Subjective Quality Assessment Relighted Human Heads AI Generated Content

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