Results 21 to 30 of about 63,443 (285)
Video Frame Interpolation with Transformer
2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2022 Video frame interpolation (VFI), which aims to synthesize intermediate frames of a video, has made remarkable progress with development of deep convolutional networks over past years. Existing methods built upon convolutional networks generally face challenges of handling large motion due to the locality of convolution operations.
Lu, Liying +4 more
openaire +2 more sources
Deep Frame Interpolation for Video Compression [PDF]
2019 Data Compression Conference (DCC), 2019 Deep neural networks have been recently proposed to solve video interpolation tasks. Given a past and future frame, such networks can be trained to successfully predict the intermediate frame(s). In the context of video compression, these architectures could be useful as an additional inter-prediction mode.
Bégaint, Jean +3 more
openaire +2 more sources
Direct Video Frame Interpolation With Multiple Latent Encoders
IEEE Access, 2021 We present a simple but effective video interpolation framework that can be applied to various types of videos including conventional videos and 360° videos.
Yong-Hoon Kwon +2 more
doaj +1 more source
Textural Detail Preservation Network for Video Frame Interpolation
IEEE Access, 2023 The subjective image quality of the Video Frame Interpolation (VFI) result depends on whether image features such as edges, textures and blobs are preserved.
Kihwan Yoon +4 more
doaj +1 more source
Depth-Aware Video Frame Interpolation [PDF]
2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2019 This work is accepted in CVPR 2019.
Bao, Wenbo +5 more
openaire +2 more sources
Optimizing Video Prediction via Video Frame Interpolation
2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2022 Video prediction is an extrapolation task that predicts future frames given past frames, and video frame interpolation is an interpolation task that estimates intermediate frames between two frames. We have witnessed the tremendous advancement of video frame interpolation, but the general video prediction in the wild is still an open question. Inspired
Wu, Yue, Wen, Qiang, Chen, Qifeng
openaire +2 more sources
A Motion Refinement Network With Local Compensation for Video Frame Interpolation
IEEE Access, 2023 Video frame interpolation (VFI) is a challenging yet promising task that involves synthesizing intermediate frames from two given frames. State-of-the-art approaches have made significant progress by directly synthesizing images using forward optical ...
Kaiqiao Wang, Peng Liu
doaj +1 more source
Multiple description video coding for stereoscopic 3D [PDF]
, 2009 In this paper, we propose an MDC schemes for stereoscopic 3D video. In the literature, MDC has previously been applied in 2D video but not so much in 3D video.
Abdul Karim, H +4 more
core +1 more source
PhaseNet for Video Frame Interpolation [PDF]
2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2018 Most approaches for video frame interpolation require accurate dense correspondences to synthesize an in-between frame. Therefore, they do not perform well in challenging scenarios with e.g. lighting changes or motion blur. Recent deep learning approaches that rely on kernels to represent motion can only alleviate these problems to some extent.
Meyer, Simone +5 more
openaire +2 more sources
BVI-VFI: A Video Quality Database for Video Frame Interpolation
IEEE Transactions on Image Processing, 2023 Video frame interpolation (VFI) is a fundamental research topic in video processing, which is currently attracting increased attention across the research community. While the development of more advanced VFI algorithms has been extensively researched, there remains little understanding of how humans perceive the quality of interpolated content and how
Duolikun Danier +2 more
openaire +4 more sources