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检索条件"任意字段=2023 IEEE International Conference on Visual Communications and Image Processing, VCIP 2023"

共 2299 条记录，以下是321-330 订阅

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Perceptual Quality Assessment for Video Frame Interpolation

Perceptual Quality Assessment for Video Frame Interpolation

引用

2023 ieee international conference on visual communications and image processing, vcip 2023

作者： Han, Jinliang Min, Xiongkuo Gao, Yixuan Jia, Jun Sun, Lei Cao, Zuowei Luo, Yonglin Zhai, Guangtao Shanghai Jiao Tong University Institute of Image Communication and Network Engineering China Tencent China

ISBN: (纸本)9798350359855

The quality of frames is significant for both research and application of video frame interpolation (VFI). In recent VFI studies, the methods of full-reference image quality assessment have generally been used to evaluate the quality of VFI frames. However, high frame rate reference videos, necessities for the full-reference methods, are difficult to obtain in most applications of VFI. To evaluate the quality of VFI frames without reference videos, a no-reference perceptual quality assessment method is proposed in this paper. This method is more compatible with VFI application and the evaluation scores from it are consistent with human subjective opinions. A new quality assessment dataset for VFI was constructed through subjective experiments firstly, to assess the opinion scores of interpolated frames. The dataset was created from triplets of frames extracted from high-quality videos using 9 state-of-The-Art VFI algorithms. The proposed method evaluates the perceptual coherence of frames incorporating the original pair of VFI inputs. Specifically, the method applies a triplet network architecture, including three parallel feature pipelines, to extract the deep perceptual features of the interpolated frame as well as the original pair of frames. Coherence similarities of the two-way parallel features are jointly calculated and optimized as a perceptual metric. In the experiments, both full-reference and no-reference quality assessment methods were tested on the new quality dataset. The results show that the proposed method achieves the best performance among all compared quality assessment methods on the dataset. © 2023 ieee.

关键词： Network architecture

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Tell Codec What Worth Compressing: Semantically Disentangled image Coding for Machine with LMMs

Tell Codec What Worth Compressing: Semantically Disentangled...

引用

2024 conference on visual communications and image processing

作者： Liu, Jinming Wei, Yuntao Lin, Junyan Zhao, Shengyang Sun, Heming Chen, Zhibo Zeng, Wenjun Jin, Xin Shanghai Jiao Tong Univ Shanghai Peoples R China Ningbo Inst Digital Twin Eastern Inst Technol Ningbo Peoples R China Univ Sci & Technol China Hefei Peoples R China Yokohama Natl Univ Yokohama Kanagawa Japan

ISBN: (纸本)9798331529543;9798331529550

We present a new image compression paradigm to achieve "intelligently coding for machine" by cleverly leveraging the common sense of Large Multimodal Models (LMMs). We are motivated by the evidence that large language/multimodal models are powerful general-purpose semantics predictors for understanding the real world. Different from traditional image compression typically optimized for human eyes, the image coding for machines (ICM) framework we focus on requires the compressed bitstream to more comply with different downstream intelligent analysis tasks. To this end, we employ LMM to tell codec what to compress: 1) first utilize the powerful semantic understanding capability of LMMs w.r.t object grounding, identification, and importance ranking via prompts, to disentangle image content before compression, 2) and then based on these semantic priors we accordingly encode and transmit objects of the image in order with a structured bitstream. In this way, diverse vision benchmarks including image classification, object detection, instance segmentation, etc., can be well supported with such a semantically structured bitstream. We dub our method "SDComp" for "Semantically Disentangled Compression", and compare it with state-of-the-art codecs on a wide variety of different vision tasks. SDComp codec leads to more flexible reconstruction results, promised decoded visual quality, and a more generic/satisfactory intelligent task-supporting ability.

关键词： image Coding for Machine Large Multimodal Model Semantically Structured Bitstream

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Near-Duplicate image Retrieval Based on Multiple Features 33

Near-Duplicate Image Retrieval Based on Multiple Features

引用

33rd ieee international conference on visual communications and image processing (ieee vcip)

作者： Zhang, Xueqing Nanjing Univ Sci & Technol Nanjing 260294 Jiangsu Peoples R China

ISBN: (纸本)9781538644584

In this paper, we propose a near-duplicate image retrieval method based on multiple features. Combining the deep features extracted from the VGG relu6 layer with the improved local feature descriptors, we attempt to simulate the nearduplicate image retrieval process of the human brain through a two-layer retrieval structure. Inspired by the proposed CROW feature, we calculate the weights on VGG shallow pooling layer and extract the interest domains for screening surf feature points. At the same time, a center weight is proposed to improve the VLAD algorithm. Experiments show that our method can not only obtain the visually similar results of an image, but also obtain the results that contain the visually prominent parts of the image.

关键词： near-duplicate retrieval interest domain center weights multiple features convolutional neural networks (CNN)

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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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BAN, A Barcode Accurate Detection Network 33

BAN, A Barcode Accurate Detection Network

引用

33rd ieee international conference on visual communications and image processing (ieee vcip)

作者： Tian, Yuan Che, Zhaohui Zhai, Guangtao Gao, Zhiyong Shanghai Jiao Tong Univ Inst Image Commu & Network Engin Shanghai Peoples R China

ISBN: (纸本)9781538644584

Barcode detection has been elaborately investigated before. Traditional hand-crafted feature based methods achieve promising performances across some simple and high-quality test databases. However, they usually fall short as a part of barcode decoding pipeline when dealing with challenging scenarios, because of the low robustness and accuracy achieved by low-level features. In this paper, we present a barcode detection network pipeline, designed to generate accurate barcode regions with category labels. We also build a rich barcodes detection dataset-Mixed Barcode Dataset. Experimental results conducted on different barcode databases show the superiority of our method in terms of F-score and decoding accuracy compared to the state of arts.

关键词： Barcode detection multi-level pooling region proposal networks object accurate detection convolutional neutral networks

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MGTN: Multi-scale Graph Transformer Network for 3D Point Cloud Semantic Segmentation

MGTN: Multi-scale Graph Transformer Network for 3D Point Clo...

引用

2024 conference on visual communications and image processing

作者： Ai, Da Qin, Siyu Nie, Zihe Yuan, Hui Liu, Ying Xian Univ Posts & Telecommun Xian Key Lab Image Proc Technol & Applicat Publ S Xian Shaanxi Peoples R China Xian Univ Posts & Telecommun Sch Commun & Informat Engn Xian Shaanxi Peoples R China Shandong Univ Sch Control Sci & Engn Jinan Shandong Peoples R China

ISBN: (纸本)9798331529543;9798331529550

The structural similarity of point clouds presents challenges in accurately recognizing and segmenting semantic information at the demarcation points of complex scenes or objects. In this study, we propose a multi-scale graph transformer network (MGTN) for 3D point cloud semantic segmentation. First, a multi-scale graph convolution (MSG-Conv) is devised to address the limitations faced by existing methods when extracting local and global features of point cloud data with varying densities simultaneously. Subsequently, we employ a graph-transformer (G-T) module to enhance edge details and spatial position information in the point cloud, thereby improving recognition accuracy for small objects and confusing elements such as columns and beams. Extensive testing on ShapeNet parts and S3DIS datasets was conducted to demonstrate the effectiveness of MGTN. Compared to the baseline network DGCNN, our proposed MGTN achieves substantial performance improvements, as evidenced by notable increases in mIoU of 1.5% and 18.5% on the ShapeNet parts and S3DIS datasets respectively. Additionally, MGTN outperforms the recent CFSA-Net by 2.3% and 3.4% on OA and mIoU respectively.

关键词： point cloud semantic segmentation multi-scale graph convolution transformer feature extraction

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Benchmarking Conventional and Learned Video Codecs with a Low-Delay Configuration

Benchmarking Conventional and Learned Video Codecs with a Lo...

引用

2024 conference on visual communications and image processing

作者： Teng, Siyue Jiang, Yuxuan Gao, Ge Zhang, Fan Davis, Thomas Liu, Zoe Bull, David Univ Bristol Visual Informat Lab Bristol BS1 5DD Avon England Visionular Inc Los Altos CA 94022 USA

ISBN: (纸本)9798331529543;9798331529550

Recent advances in video compression have seen significant coding performance improvements with the development of new standards and learning-based video codecs. However, most of these works focus on application scenarios that allow a certain amount of system delay (e.g., Random Access mode in MPEG codecs), which is not always acceptable for live delivery. This paper conducts a comparative study of state-of-the-art conventional and learned video coding methods based on a low delay configuration. Specifically, this study includes two MPEG standard codecs (H.266/VVC VTM and JVET ECM), two AOM codecs (AV1 libaom and AVM), and two recent neural video coding models (DCVC-DC and DCVC-FM). To allow a fair and meaningful comparison, the evaluation was performed on test sequences defined in the AOM and MPEG common test conditions in the YCbCr 4:2:0 color space. The evaluation results show that the JVET ECM codecs offer the best overall coding performance among all codecs tested, with a 16.1% (based on PSNR) average BD-rate saving over AOM AVM, and 11.0% over DCVC-FM. We also observed inconsistent performance with the learned video codecs, DCVC-DC and DCVC-FM, for test content with large background motions.

关键词： Video compression low delay codec comparison learned video compression

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On 2D-3D image Feature Detections for image-To-Geometry Registration in Virtual Dental Model

On 2D-3D Image Feature Detections for Image-To-Geometry Regi...

引用

ieee international conference on visual communications and image processing (vcip)

作者： Tang, Hui Hsung, Tai-Chiu Lam, Walter Y. H. Cheng, Leo Y. Y. Pow, Edmond H. N. Guangdong Univ Technol Sch Informat Engn Guangzhou Peoples R China Univ Hong Kong Fac Dent Hong Kong Peoples R China

ISBN: (纸本)9781728180687

3D digital smile design (DSD) gains great interest in dentistry because it enables esthetic design of teeth and gum. However, the color texture of teeth and gum is often lost/distorted in the digitization process. Recently, the image-to-geometry registration shade mapping (IGRSM) method was proposed for registering color texture from 2D photography to 3D mesh model. It allows better control of illumination and color calibration for automatic teeth shade matching. In this paper, we investigate automated techniques to find the correspondences between 3D tooth model and color intraoral photographs for accurately perform the IGRSM. We propose to use the tooth cusp tips as the correspondence points for the IGR because they can be reliably detected both in 2D photography and 3D surface scan. A modified gradient descent method with directional priority (GDDP) and region growing are developed to find the 3D correspondence points. For the 2D image, the tooth tips contour lines are extracted based on luminosity and chromaticity, the contour peaks are then detected as the correspondence points. From the experimental results, the proposed method shows excellent accuracy in detecting the correspondence points between 2D photography and 3D tooth model. The average registration error is less than 15 pixels for 4752x3168 size intraoral image.

关键词： 3D digital smile design image to geometry registration Tooth Shade Intraoral scan Range image

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A Review of Breast Cancer Detection in Medical images 33

A Review of Breast Cancer Detection in Medical Images

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33rd ieee international conference on visual communications and image processing (ieee vcip)

作者： Lu, Yao Li, Jia-Yu Su, Yu-Ting Liu, An-An Tianjin Univ Sch Elect & Informat Engn Tianjin Peoples R China

ISBN: (纸本)9781538644584

Breast cancer is a malignant tumor that occurs in the glandular epithelium of the breast. It is considered to be one of the most common cancers affecting women in the world. However, there is not an effective way to cure breast cancer yet, the key to reducing the risk of death is the early detection and diagnosis of breast cancer. Accurate diagnosis of breast cancer normally requires analysis of medical images of different modalities. There is a great need of automated system that could analyze these images accurately and rapidly. In this paper, we introduce some commonly used medical imaging methods for diagnosis of breast cancer, and based on them we investigate some recently proposed approaches for breast cancer detection with computer vision and machine learning techniques. Finally, we compare and analyze the detection performance of different methods on histological images and mammograph images respectively.

关键词： Breast Cancer Detection Histological image Mammograph image Ultrasound image Machine Learning

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image-Prompt Integration Network with Self-Ranking and Inter-Ranking Loss for AI-Generated image Quality Assessment

Image-Prompt Integration Network with Self-Ranking and Inter...

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2024 conference on visual communications and image processing

作者： Zhou, Tianwei Yao, Xizhang Tan, Songbai Ding, Xiaoying Yue, Guanghui Shenzhen Univ Coll Management Shenzhen Peoples R China Zhongnan Univ Econ & Law Sch Informat Engn Wuhan Peoples R China Shenzhen Univ Sch Biomed Engn Shenzhen Peoples R China

ISBN: (纸本)9798331529543;9798331529550

AI-generated images (AGIs) are increasingly utilized across diverse domains due to their ability to quickly produce high-quality visuals. However, assessing the quality of AGIs remains challenging due to their inherent variability and distinctive distortions. To address these challenges, we propose a novel AGI quality assessment method named SIRQA, which enhances feature representation by integrating visual features with textual prompts, effectively measureing the alignment between the generated images and the described content to improve the precision of quality assessment. Specifically, SIRQA employs self-ranking and inter-ranking mechanisms to refine feature representation. The self-ranking mechanism maintains consistency between feature distances and sampling scales, making sure that features from similar sampling scales are positioned closer together. Additionally, inter-ranking mechanism sorts the weighted similarity scores between images and prompts to align with the ranking in the label space. Extensive experiments on the AGIQA3K and PKUI2IQA datasets show that our SIRQA outperforms eight state-of-the-art algorithms in terms of both Spearman's rank correlation coefficient (SRCC) and Pearson linear correlation coefficient (PLCC).

关键词： AI-generated images image quality assessment multi-scale feature image-prompt integration

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