2022 IEEE International Conference on Visual Communications and Image Processing (VCIP)最新文献_第7页

Human pose based video compression via forward-referencing using deep learning 基于深度学习前向参考的基于人体姿态的视频压缩

2022 IEEE International Conference on Visual Communications and Image Processing (VCIP) Pub Date : 2022-12-13 DOI: 10.1109/VCIP56404.2022.10008897

S. Rajin, M. Murshed, M. Paul, S. Teng, Jiangang Ma

{"title":"Human pose based video compression via forward-referencing using deep learning","authors":"S. Rajin, M. Murshed, M. Paul, S. Teng, Jiangang Ma","doi":"10.1109/VCIP56404.2022.10008897","DOIUrl":"https://doi.org/10.1109/VCIP56404.2022.10008897","url":null,"abstract":"To exploit high temporal correlations in video frames of the same scene, the current frame is predicted from the already-encoded reference frames using block-based motion estimation and compensation techniques. While this approach can efficiently exploit the translation motion of the moving objects, it is susceptible to other types of affine motion and object occlusion/deocclusion. Recently, deep learning has been used to model the high-level structure of human pose in specific actions from short videos and then generate virtual frames in future time by predicting the pose using a generative adversarial network (GAN). Therefore, modelling the high-level structure of human pose is able to exploit semantic correlation by predicting human actions and determining its trajectory. Video surveillance applications will benefit as stored “big” surveillance data can be compressed by estimating human pose trajectories and generating future frames through semantic correlation. This paper explores a new way of video coding by modelling human pose from the already-encoded frames and using the generated frame at the current time as an additional forward-referencing frame. It is expected that the proposed approach can overcome the limitations of the traditional backward-referencing frames by predicting the blocks containing the moving objects with lower residuals. Our experimental results show that the proposed approach can achieve on average up to 2.83 dB PSNR gain and 25.93% bitrate savings for high motion video sequences compared to standard video coding.","PeriodicalId":269379,"journal":{"name":"2022 IEEE International Conference on Visual Communications and Image Processing (VCIP)","volume":"34 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123662869","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

CNN-Based Post-Processing Filter for Video Compression with Multi-Scale Feature Representation 基于cnn的多尺度特征表示视频压缩后处理滤波器

2022 IEEE International Conference on Visual Communications and Image Processing (VCIP) Pub Date : 2022-12-13 DOI: 10.1109/VCIP56404.2022.10008797

Zhanyuan Qi, Cheolkon Jung, Yang Liu, Ming Li

{"title":"CNN-Based Post-Processing Filter for Video Compression with Multi-Scale Feature Representation","authors":"Zhanyuan Qi, Cheolkon Jung, Yang Liu, Ming Li","doi":"10.1109/VCIP56404.2022.10008797","DOIUrl":"https://doi.org/10.1109/VCIP56404.2022.10008797","url":null,"abstract":"In this paper, we propose a convolutional neural network (CNN)-based post-processing filter for video compression with multi-scale feature representation. The discrete wavelet transform (DWT) decomposes an image into multi-frequency and multi-directional sub-bands, and can figure out artifacts caused by video compression with multi-scale feature representation. Thus, we combine DWT with CNN and construct two sub-networks: Step-like sub-band network (SLSB) and mixed enhancement network (ME). SLSB takes the wavelet subbands as input, and feeds them into the Res2Net group (R2NG) from high frequency to low frequency. R2NG consists of Res2Net modules and adopts spatial and channel attentions to adaptively enhance features. We combine the high frequency sub-band output with the low frequency sub-band in R2NG to capture multi-scale features. ME uses mixed convolution composed of dilated convolution and standard convolution as the basic block to expand the receptive field without blind spots in dilated convolution and further improve the reconstruction quality. Experimental results demonstrate that the proposed CNN filter achieves average 2.13%, 2.63%, 2.99%, 4.8%, 3.72% and 4.5% BD-rate reductions over VTM 11.0-NNVC anchor for Y channel on A1, A2, B, C, D and E classes of the common test conditions (CTC) in AI, RA and LDP configurations, respectively.","PeriodicalId":269379,"journal":{"name":"2022 IEEE International Conference on Visual Communications and Image Processing (VCIP)","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117137861","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

Annotating Only at Definite Pixels: A Novel Weakly Supervised Semantic Segmentation Method for Sea Fog Recognition 一种用于海雾识别的弱监督语义分割新方法

2022 IEEE International Conference on Visual Communications and Image Processing (VCIP) Pub Date : 2022-12-13 DOI: 10.1109/VCIP56404.2022.10008863

Xun Zhu, Mengqiu Xu, Ming Wu, Chuang Zhang, Bin Zhang

{"title":"Annotating Only at Definite Pixels: A Novel Weakly Supervised Semantic Segmentation Method for Sea Fog Recognition","authors":"Xun Zhu, Mengqiu Xu, Ming Wu, Chuang Zhang, Bin Zhang","doi":"10.1109/VCIP56404.2022.10008863","DOIUrl":"https://doi.org/10.1109/VCIP56404.2022.10008863","url":null,"abstract":"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.","PeriodicalId":269379,"journal":{"name":"2022 IEEE International Conference on Visual Communications and Image Processing (VCIP)","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121765614","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

Near-lossless Point Cloud Geometry Compression Based on Adaptive Residual Compensation 基于自适应残差补偿的近无损点云几何压缩

2022 IEEE International Conference on Visual Communications and Image Processing (VCIP) Pub Date : 2022-12-13 DOI: 10.1109/VCIP56404.2022.10008796

Dingquan Li, Jing Wang, Ge Li

引用次数: 0

MRIQA: Subjective Method and Objective Model for Magnetic Resonance Image Quality Assessment 磁共振图像质量评价的主观方法和客观模型

2022 IEEE International Conference on Visual Communications and Image Processing (VCIP) Pub Date : 2022-12-13 DOI: 10.1109/VCIP56404.2022.10008885

Qi Chen, F. Liu, Huiyu Duan, Yao Wang, Xiongkuo Min, Yan Zhou, Guangtao Zhai

{"title":"MRIQA: Subjective Method and Objective Model for Magnetic Resonance Image Quality Assessment","authors":"Qi Chen, F. Liu, Huiyu Duan, Yao Wang, Xiongkuo Min, Yan Zhou, Guangtao Zhai","doi":"10.1109/VCIP56404.2022.10008885","DOIUrl":"https://doi.org/10.1109/VCIP56404.2022.10008885","url":null,"abstract":"Magnetic Resonance Imaging (MRI) is widely used for medical diagnosis, staging and follow-up of disease. However, MRI images may have artifacts due to various reasons such as patient movement or machine distortion, which may be unintentionally introduced during the procedure of medical image acquisition, processing, etc. These artifacts may affect the effectiveness of diagnosis or even cause false diagnosis. To solve this problem, we propose a general medical image quality assessment (MIQA) methodology, including subjective MIQA procedures and objective MIQA algorithms. We further apply this methodology to MRI images in this paper due to its widespread use in practical applications. We first establish a magnetic resonance imaging quality assessment (MRIQA) database, which contains 3809 MRI images. Then a subjective image quality assessment experiment is conducted by expert doctors according to the diagnostic value of these images, which split all MRI images into 1285 low quality images and 2524 high quality images. We then conduct a baseline deep learning experiment, and propose an attention based MIQANet model to automatically separate MRI images into high quality and low quality based on their diagnosis value. Our proposed method achieves a great quality assessment accuracy of 96.59%. The constructed MRIQA database and proposed MIQA model will be public available to further promote medical IQA research.","PeriodicalId":269379,"journal":{"name":"2022 IEEE International Conference on Visual Communications and Image Processing (VCIP)","volume":"108 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127960384","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A Fast and Effective Framework for Camera Calibration in Sport Videos 一种快速有效的运动视频摄像机标定框架

2022 IEEE International Conference on Visual Communications and Image Processing (VCIP) Pub Date : 2022-12-13 DOI: 10.1109/VCIP56404.2022.10008882

Neng Zhang, E. Izquierdo

引用次数: 0

Fast CU Partition Method Based on Extra Trees for VVC Intra Coding 基于额外树的VVC内部编码快速CU划分方法

2022 IEEE International Conference on Visual Communications and Image Processing (VCIP) Pub Date : 2022-12-13 DOI: 10.1109/VCIP56404.2022.10008800

Kaijie Wang, Hong Liang, Saiping Zhang, Fuzheng Yang

引用次数: 0

PickDet: A Detection Framework for Aerial-view Scene PickDet:一个鸟瞰场景的检测框架

2022 IEEE International Conference on Visual Communications and Image Processing (VCIP) Pub Date : 2022-12-13 DOI: 10.1109/VCIP56404.2022.10008902

Cheng Lyu, Xiao Deng, Shizun Wang, Ming Wu, Chuang Zhang

{"title":"PickDet: A Detection Framework for Aerial-view Scene","authors":"Cheng Lyu, Xiao Deng, Shizun Wang, Ming Wu, Chuang Zhang","doi":"10.1109/VCIP56404.2022.10008902","DOIUrl":"https://doi.org/10.1109/VCIP56404.2022.10008902","url":null,"abstract":"Detecting objects in the aerial-view scene is challenging for the objects usually have small scales relative to the image, making it hard to achieve high accuracy in full-image detection. Slice detection tries to overcome this by cutting the full image into slices before detecting them, but objects are sparsely distributed and usually clustered in local areas, a large number of background areas without objects can be ignored to improve detection efficiency. In this paper, we present PickDet, a framework for efficient and effective object detection in the aerial-view scene, which only chooses slices containing objects to conduct detection. The key components of PickDet include a lightweight convolutional network (PickNet), a screening strategy (SoftPick), and fine-tuned detectors. Given slices of aerial-view images, PickNet first outputs the probability of object existence. Then SoftPick conducts a double-threshold screening strategy to pick the slices which contain objects. Finally, all picked slices are fed into the detector in parallel and full-image detection is used as an auxiliary mean. Compared with previous methods, PickDet achieves higher accuracy and more efficiency in the aerial-view scene. We evaluate PickDet on Visdrone and Oiltank datasets, experiments show that PickDet can result in up to 28.0% AP improvement compared to full-image detection, and can result in up to 2.9% AP increase and up to 5 times inference speedup compared to slice detection.","PeriodicalId":269379,"journal":{"name":"2022 IEEE International Conference on Visual Communications and Image Processing (VCIP)","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129561055","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

One Shot Object Detection Via Hierarchical Adaptive Alignment 通过分层自适应对齐的单镜头目标检测

2022 IEEE International Conference on Visual Communications and Image Processing (VCIP) Pub Date : 2022-12-13 DOI: 10.1109/VCIP56404.2022.10008884

Enquan Zhang, Cheolkon Jung

引用次数: 0

Fast Inter Prediction Mode Decision Method Based On Random Forest For H.266/VVC 基于随机森林的H.266/VVC间预测模式快速决策方法

2022 IEEE International Conference on Visual Communications and Image Processing (VCIP) Pub Date : 2022-12-13 DOI: 10.1109/VCIP56404.2022.10008840

Kundan Xie, Jianquan Zhou, Saiping Zhang, Fuzheng Yang

引用次数: 0