2021 International Conference on Visual Communications and Image Processing (VCIP)最新文献

Polynomial Image-Based Rendering for non-Lambertian Objects 非lambertian对象的多项式图像渲染

2021 International Conference on Visual Communications and Image Processing (VCIP) Pub Date : 2021-12-05 DOI: 10.1109/VCIP53242.2021.9675371

Sarah Fachada, Daniele Bonatto, Mehrdad Teratani, G. Lafruit

引用次数: 5

An Intra String Copy Approach for SCC in AVS3 AVS3中SCC的内部字符串复制方法

2021 International Conference on Visual Communications and Image Processing (VCIP) Pub Date : 2021-12-05 DOI: 10.1109/VCIP53242.2021.9675427

Liping Zhao, Kailun Zhou, Qingyang Zhou, Huihui Wang, Tao Lin

引用次数: 0

Encoder-Decoder Joint Enhancement for Video Chat 视频聊天的编码器-解码器联合增强

2021 International Conference on Visual Communications and Image Processing (VCIP) Pub Date : 2021-12-05 DOI: 10.1109/VCIP53242.2021.9675448

Zhenghao Zhang, Zhao Wang, Yan Ye, Shiqi Wang, Changwen Zheng

引用次数: 0

Stereoscopic Video Quality Assessment with Multi-level Binocular Fusion Network Considering Disparity and Multi-scale Information 考虑视差和多尺度信息的多级双目融合网络立体视频质量评估

2021 International Conference on Visual Communications and Image Processing (VCIP) Pub Date : 2021-12-05 DOI: 10.1109/VCIP53242.2021.9675404

Yingjie Feng, Sumei Li

引用次数: 3

Deep Inter Prediction via Reference Frame Interpolation for Blurry Video Coding 基于参考帧插值的模糊视频编码深度预测

2021 International Conference on Visual Communications and Image Processing (VCIP) Pub Date : 2021-12-05 DOI: 10.1109/VCIP53242.2021.9675429

Zezhi Zhu, Lili Zhao, Xuhu Lin, Xuezhou Guo, Jianwen Chen

{"title":"Deep Inter Prediction via Reference Frame Interpolation for Blurry Video Coding","authors":"Zezhi Zhu, Lili Zhao, Xuhu Lin, Xuezhou Guo, Jianwen Chen","doi":"10.1109/VCIP53242.2021.9675429","DOIUrl":"https://doi.org/10.1109/VCIP53242.2021.9675429","url":null,"abstract":"In High Efficiency Video Coding (HEVC), inter prediction is an important module for removing temporal redundancy. The accuracy of inter prediction is much affected by the similarity between the current and reference frames. However, for blurry videos, the performance of inter coding will be degraded by varying motion blur, which is derived from camera shake or the acceleration of objects in the scene. To address this problem, we propose to synthesize additional reference frame via the frame interpolation network. The synthesized reference frame is added into reference picture lists to supply more credible reference candidate, and the searching mechanism for motion candidates is changed accordingly. In addition, to make our interpolation network more robust to various inputs with different compression artifacts, we establish a new blurry video database to train our network. With the well-trained frame interpolation network, compared with the reference software HM-16.9, the proposed method achieves on average 1.55% BD-rate reduction under random access (RA) configuration for blurry videos, and also obtains on average 0.75% BD-rate reduction for common test sequences.","PeriodicalId":114062,"journal":{"name":"2021 International Conference on Visual Communications and Image Processing (VCIP)","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116550543","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

A Study on 4D Light Field Compression Using Multi-focus Images and Reference Views 基于多聚焦图像和参考视图的四维光场压缩研究

2021 International Conference on Visual Communications and Image Processing (VCIP) Pub Date : 2021-12-05 DOI: 10.1109/VCIP53242.2021.9675378

Shuho Umebayashi, K. Kodama, T. Hamamoto

{"title":"A Study on 4D Light Field Compression Using Multi-focus Images and Reference Views","authors":"Shuho Umebayashi, K. Kodama, T. Hamamoto","doi":"10.1109/VCIP53242.2021.9675378","DOIUrl":"https://doi.org/10.1109/VCIP53242.2021.9675378","url":null,"abstract":"We propose a novel method of light field compression using multi-focus images and reference views. Light fields enable us to observe scenes from various viewpoints. However, it generally consists of 4D enormous data, that are not suitable for storing or transmitting without effective compression at relatively low bit-rates. On the other hand, 4D light fields are essentially redundant because it includes just 3D scene information. While robust 3D scene estimation such as depth recovery from light fields is not so easy, a method of reconstructing light fields directly from 3D information composed of multi-focus images without any scene estimation is successfully derived. Based on the method, we previously proposed light field compression via multi-focus images as effective representation of 3D scenes. Actually, its high performance can be seen only at very low bit-rates, because there exists some degradation of low frequency components and occluded regions on light fields predicted from multi-focus images. In this paper, we study higher quality light field compression by using reference views to improve quality of the prediction from multi-focus images. Our contribution is twofold: first, our improved method can keep good performance of 4D light field compression at a wider range of low bit-rates than the previous one working effectively only for very low bit-rates; second, we clarify how the proposed method can improve its performance continuously by introducing recent video codec such as HEVC and VVC into our compression framework, that does not depend on 3D-SPIHT previously adopted for the corresponding component. We show experimental results by using synthetic and real images, where quality of reconstructed light fields is evaluated by PSNR and SSIM for analyzing characteristics of our novel method well. We notice that it is much superior to light field compression using HEVC directly at low bit-rates regardless of its light field scan order.","PeriodicalId":114062,"journal":{"name":"2021 International Conference on Visual Communications and Image Processing (VCIP)","volume":"2013 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128218512","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

Binocular Visual Mechanism Guided No-Reference Stereoscopic Image Quality Assessment Considering Spatial Saliency 考虑空间显著性的双目视觉机制引导无参考立体图像质量评价

2021 International Conference on Visual Communications and Image Processing (VCIP) Pub Date : 2021-12-05 DOI: 10.1109/vcip53242.2021.9675338

Jinhui Feng, Sumei Li, Yongli Chang

引用次数: 0

Stereo Image Super-Resolution Based on Pixel-Wise Knowledge Distillation Strategy 基于逐像素知识蒸馏策略的立体图像超分辨率

2021 International Conference on Visual Communications and Image Processing (VCIP) Pub Date : 2021-12-05 DOI: 10.1109/VCIP53242.2021.9675446

Li Ma, Sumei Li

{"title":"Stereo Image Super-Resolution Based on Pixel-Wise Knowledge Distillation Strategy","authors":"Li Ma, Sumei Li","doi":"10.1109/VCIP53242.2021.9675446","DOIUrl":"https://doi.org/10.1109/VCIP53242.2021.9675446","url":null,"abstract":"In stereo image super-resolution (SR), it is equally important to utilize intra-view and cross-view information. However, most existing methods only focus on the exploration of cross-view information and neglect the full mining of intra-view information, which limits the reconstruction performance of these methods. Since single image SR (SISR) methods are powerful in intra-view information exploitation, we propose to introduce the knowledge distillation strategy to transfer the knowledge of a SISR network (teacher network) to a stereo image SR network (student network). With the help of the teacher network, the student network can easily learn more intra-view information. Specifically, we propose pixel-wise distillation as the implementation method, which not only improves the intra-view information extraction ability of student network, but also ensures the effective learning of cross-view information. Moreover, we propose a lightweight student network named Adaptive Residual Feature Aggregation network (ARFAnet). Its main unit, the ARFA module, can aggregate informative residual features and produce more representative features for image reconstruction. Experimental results demonstrate that our teacher-student network achieves state-of-the-art performance on all benchmark datasets.","PeriodicalId":114062,"journal":{"name":"2021 International Conference on Visual Communications and Image Processing (VCIP)","volume":"36 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129482859","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

DensER: Density-imbalance-Eased Representation for LiDAR-based Whole Scene Upsampling DensER:基于激光雷达的全场景上采样的密度不平衡简化表示

2021 International Conference on Visual Communications and Image Processing (VCIP) Pub Date : 2021-12-05 DOI: 10.1109/VCIP53242.2021.9675334

Tso-Yuan Chen, Ching-Chun Hsiao, Wen-Huang Cheng, Hong-Han Shuai, Peter Chen, Ching-Chun Huang

{"title":"DensER: Density-imbalance-Eased Representation for LiDAR-based Whole Scene Upsampling","authors":"Tso-Yuan Chen, Ching-Chun Hsiao, Wen-Huang Cheng, Hong-Han Shuai, Peter Chen, Ching-Chun Huang","doi":"10.1109/VCIP53242.2021.9675334","DOIUrl":"https://doi.org/10.1109/VCIP53242.2021.9675334","url":null,"abstract":"With the development of depth sensors, 3D point cloud upsampling that generates a high-resolution point cloud given a sparse input becomes emergent. However, many previous works focused on single 3D object reconstruction and refinement. Although a few recent works began to discuss 3D structure refine-ment for a more complex scene, they do not target LiDAR-based point clouds, which have density imbalance issues from near to far. This paper proposed DensER, a Density-imbalance-Eased regional Representation. Notably, to learn robust representations and model local geometry under imbalance point density, we designed density-aware multiple receptive fields to extract the regional features. Moreover, founded on the patch reoccurrence property of a nature scene, we proposed a density-aided attentive module to enrich the extracted features of point-sparse areas by referring to other non-local regions. Finally, by coupling with novel manifold-based upsamplers, DensER shows the ability to super-resolve LiDAR-based whole-scene point clouds. The exper-imental results show DensER outperforms related works both in qualitative and quantitative evaluation. We also demonstrate that the enhanced point clouds can improve downstream tasks such as 3D object detection and depth completion.","PeriodicalId":114062,"journal":{"name":"2021 International Conference on Visual Communications and Image Processing (VCIP)","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129340430","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: 2

Revisiting Flipping Strategy for Learning-based Stereo Depth Estimation 基于学习的立体深度估计翻转策略研究

2021 International Conference on Visual Communications and Image Processing (VCIP) Pub Date : 2021-12-05 DOI: 10.1109/VCIP53242.2021.9675450

Yue Li, Yueyi Zhang, Zhiwei Xiong

引用次数: 2