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EDGE-PRESERVING SINGLE DEPTH image INTERPOLATION

EDGE-PRESERVING SINGLE DEPTH IMAGE INTERPOLATION

引用

IEEE International conference on visual communications and image processing (VCIP)

作者： Zhong, Gang Yu, Li Zhou, Peng Huazhong Univ Sci & Technol Dept Elect & Informat Engn Wuhan Peoples R China

ISBN: (纸本)9781479902880

Depth image upsampling is an important issue in three-dimensional (3D) applications. However, edge blurring artifacts are still challenging problems in depth image upsampling, resulting in jagged artifacts in synthesized views which produce unpleasant visual perception. In this paper, an edge-preserving single depth image interpolation (ESDI) method is proposed. Specifically, local planar hypothesis (LPH) assuming that depth in natural scene are clustered as local planar planes is first explored. Then finite candidates generation (FCG) is proposed to generate limited discrete values satisfied with LPH to interpolated pixels. At last, the optimal combination of candidates is formulated as an energy minimization problem with a constraint in gradient domain, solved by iterated conditional modes (ICM) algorithm. Experiments demonstrate that ESDI achieves high resolution (HR) depth image with clear and sharp edges, and produces synthesized views with desirable quality.

关键词： Depth image interpolation local planar hypothesis gradient profile prior gradient domain

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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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Annotating Only at Definite Pixels: A Novel Weakly Supervised Semantic Segmentation Method for Sea Fog Recognition

Annotating Only at Definite Pixels: A Novel Weakly Supervise...

引用

IEEE International conference on visual communications and image processing (VCIP)

作者： Zhu, Xun Xu, Mengqiu Wu, Ming Zhang, Chuang Zhang, Bin Beijing Univ Posts & Telecommun Beijing Peoples R China

ISBN: (纸本)9781665475921

Sea fog recognition is a challenging and significant semantic segmentation task in remote sensing images. The fully supervised learning method relies on the pixel-level label, which is labor-intensive and time-consuming. Moreover, it is impossible to accurately annotate all pixels of the sea fog region due to the limited ability of the human eye to distinguish between low clouds and sea fog. In this paper, we propose a novel approach of point-based annotation for weakly supervised semantic segmentation with the auxiliary information of International Comprehensive Ocean-Atmosphere Data Set (ICOADS) visibility data. It only needs several definite points for both foreground and background, which significantly reduces the annotation cost of manpower. We conduct extensive experiments on Himawari-8 satellite remote sensing images to demonstrate the effectiveness of our annotation method. The mean intersection over union (mIoU) and overall recognition accuracy of our annotation method reach 82.72% and 95.18%, respectively. Compared with the fully supervised learning method, the accuracy and the recognition rate of sea fog area are improved with a maximum increase of 7.69% and 9.69%, respectively.

关键词： sea fog recognition weakly supervised learning point annotation semantic segmentation remote sensing image

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IoU-CLIP: IoU-Aware Language-image Model Tuning for Open Vocabulary Object Detection

IoU-CLIP: IoU-Aware Language-Image Model Tuning for Open Voc...

引用

2024 conference on visual communications and image processing

作者： He, Mingzhou Wu, Qingbo Ngan, King Ngi Xiao, Yiming Meng, Fanman Qiu, Heqian Li, Hongliang Univ Elect Sci & Technol China Sch Informat & Commun Engn Chengdu 611731 Peoples R China

ISBN: (纸本)9798331529543;9798331529550

Open vocabulary object detection (OVD), which detects novel categories through detectors trained on base categories, has achieved remarkable advancement attributable to large-scale vision-language models, such as CLIP. The prior OVD works mainly focused on improving the classification accuracy of proposals, ignoring the ability of localization for novel categories. In this work, we propose IoU-aware language-image model tuning (IoU-CLIP) for open vocabulary object detection. Specifically, we construct a region image dataset with different IoU and adopt IoU values as labels to fine-tune the CLIP model to learn IoU-aware and class-agnostic semantic prompts and visual embeddings. The fine-tuned IoU-CLIP can predict IoU scores for proposals, which interact with classification scores. Meanwhile, IoU-aware and class-agnostic visual embeddings are utilized for box regression to enhance the generalization of the localization capability. We evaluate our method on the COCO and LVIS OVD benchmarks, outperforming the baseline (RegionCLIP) by 5.5% AP(50) and 5.8% AP on novel categories, respectively, achieving state-of-the-art performance.

关键词： object detection open vocabulary IoU-aware class-agnostic semantic prompts

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

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

引用

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

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

引用

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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A MULTI-MODEL FUSION FRAMEWORK FOR NIR-TO-RGB TRANSLATION

A MULTI-MODEL FUSION FRAMEWORK FOR NIR-TO-RGB TRANSLATION

引用

IEEE International conference on visual communications and image processing (VCIP)

作者： Yan, Longbin Wang, Xiuheng Zhao, Min Liu, Shumin Chen, Jie Northwestern Polytech Univ Sch Marine Sci & Technol CIAIC Xian Peoples R China Shanghai Shengyao Intelligent Technol Co Ltd Shanghai Peoples R China Blueye Intelligence Zhenjiang Jiangsu Peoples R China

ISBN: (纸本)9781728180687

Near-infrared (NIR) images provide spectral information beyond the visible light spectrum and thus are useful in many applications. However, single-channel NIR images contain less information per pixel than RGB images and lack visibility for human perception. Transforming NIR images to RGB images is necessary for performing further analysis and computer vision tasks. In this work, we propose a novel NIR-to-RGB translation method. It contains two sub-networks and a fusion operator. Specifically, a U-net based neural network is used to learn the texture information while a CycleGAN based neural network is adopted to excavate the color information. Finally, a guided filter based fusion strategy is applied to fuse the outputs of these two neural networks. Experiment results show that our proposed method achieves superior NIR-to-RGB translation performance.

关键词： NIR-to-RGB translation U-net CycleGAN guided filter image fusion

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Coding-adaptive transforms

Coding-adaptive transforms

引用

conference on visual communications and image processing 2004

作者： Lelescu, D Bossen, F DoCoMo Commun Labs USA San Jose CA USA

ISBN: (纸本)0819452114

Transform coding plays a central role in image and video coding technologies. Various transforms. whether fixed or adaptive, have been utilized for video compression. However, these transforms are inherently suboptimal in a coding-efficiency sense, given that their design does not explicitly take into account the entire transform coding model. Thus, commonly used transforms such as the DCT have desirable properties, but were designed without the full consideration of the other transform coding elements including quantization and entropy coding. Although these transforms have good performance in a rate-distortion sense, as demonstrated by today's image and video coding standards, we show that superior transforms can be designed based on the complete transform coding model. We present a new class of transforms called coding-adaptive transforms, that are derived under an optimality criterion which incorporates the elements of transform coding and is formulated for coding efficiency. Additionally characteristics of the new transforms such as adaptivity, generalization, and robustness are discussed.

关键词： Transform theory Video coding Video Quantization Computed tomography Computer programming image compression Algorithm development Motion models Performance modeling

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A NOVEL image TAG SALIENCY RANKING ALGORITHM BASED ON SPARSE REPRESENTATION

A NOVEL IMAGE TAG SALIENCY RANKING ALGORITHM BASED ON SPARSE...

引用

IEEE International conference on visual communications and image processing (VCIP)

作者： Wang, Caixia Song, Zehai Feng, Songhe Lang, Congyan Yan, Shuicheng Beijing Jiaotong Univ Beijing Peoples R China Natl Univ Singapore Singapore Singapore

ISBN: (纸本)9781479902880

As the explosive growth of the web image data, image tag ranking used for image retrieval accurately from mass images is becoming an active research topic. However, the existing ranking approaches are not very ideal, which remains to be improved. This paper proposed a new image tag saliency ranking algorithm based on sparse representation. we firstly propagate labels from image-level to region-level via Multi-instance Learning driven by sparse representation, which means reconstructing the target instance from positive bag via the sparse linear combination of all the instances from training set, instances with nonzero reconstruction coefficients are considered to be similar to the target instance;then visual attention model is used for tag saliency analysis. Comparing with the existing approaches, the proposed method achieves a better effect and shows a better performance.

关键词： tag saliency ranking sparse representation multi-instance learning Diverse Density visual attention model

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