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The Experts below are selected from a list of 53187 Experts worldwide ranked by ideXlab platform

Minghsuan Yang - One of the best experts on this subject based on the ideXlab platform.

  • memc net motion estimation and motion compensation driven neural network for video interpolation and enhancement
    IEEE Transactions on Pattern Analysis and Machine Intelligence, 2021
    Co-Authors: Wenbo Bao, Weisheng Lai, Xiaoyun Zhang, Zhiyong Gao, Minghsuan Yang
    Abstract:

    Motion estimation (ME) and motion compensation (MC) have been widely used for classical video Frame interpolation systems over the past decades. Recently, a number of data-driven Frame interpolation methods based on convolutional neural networks have been proposed. However, existing learning based methods typically estimate either flow or compensation kernels, thereby limiting performance on both computational efficiency and interpolation accuracy. In this work, we propose a motion estimation and compensation driven neural network for video Frame interpolation. A novel adaptive warping layer is developed to integrate both optical flow and interpolation kernels to synthesize Target Frame pixels. This layer is fully differentiable such that both the flow and kernel estimation networks can be optimized jointly. The proposed model benefits from the advantages of motion estimation and compensation methods without using hand-crafted features. Compared to existing methods, our approach is computationally efficient and able to generate more visually appealing results. Furthermore, the proposed MEMC-Net architecture can be seamlessly adapted to several video enhancement tasks, e.g., super-resolution, denoising, and deblocking. Extensive quantitative and qualitative evaluations demonstrate that the proposed method performs favorably against the state-of-the-art video Frame interpolation and enhancement algorithms on a wide range of datasets.

  • memc net motion estimation and motion compensation driven neural network for video interpolation and enhancement
    arXiv: Computer Vision and Pattern Recognition, 2018
    Co-Authors: Wenbo Bao, Weisheng Lai, Xiaoyun Zhang, Zhiyong Gao, Minghsuan Yang
    Abstract:

    Motion estimation (ME) and motion compensation (MC) have been widely used for classical video Frame interpolation systems over the past decades. Recently, a number of data-driven Frame interpolation methods based on convolutional neural networks have been proposed. However, existing learning based methods typically estimate either flow or compensation kernels, thereby limiting performance on both computational efficiency and interpolation accuracy. In this work, we propose a motion estimation and compensation driven neural network for video Frame interpolation. A novel adaptive warping layer is developed to integrate both optical flow and interpolation kernels to synthesize Target Frame pixels. This layer is fully differentiable such that both the flow and kernel estimation networks can be optimized jointly. The proposed model benefits from the advantages of motion estimation and compensation methods without using hand-crafted features. Compared to existing methods, our approach is computationally efficient and able to generate more visually appealing results. Furthermore, the proposed MEMC-Net can be seamlessly adapted to several video enhancement tasks, e.g., super-resolution, denoising, and deblocking. Extensive quantitative and qualitative evaluations demonstrate that the proposed method performs favorably against the state-of-the-art video Frame interpolation and enhancement algorithms on a wide range of datasets.

Wenbo Bao - One of the best experts on this subject based on the ideXlab platform.

  • memc net motion estimation and motion compensation driven neural network for video interpolation and enhancement
    IEEE Transactions on Pattern Analysis and Machine Intelligence, 2021
    Co-Authors: Wenbo Bao, Weisheng Lai, Xiaoyun Zhang, Zhiyong Gao, Minghsuan Yang
    Abstract:

    Motion estimation (ME) and motion compensation (MC) have been widely used for classical video Frame interpolation systems over the past decades. Recently, a number of data-driven Frame interpolation methods based on convolutional neural networks have been proposed. However, existing learning based methods typically estimate either flow or compensation kernels, thereby limiting performance on both computational efficiency and interpolation accuracy. In this work, we propose a motion estimation and compensation driven neural network for video Frame interpolation. A novel adaptive warping layer is developed to integrate both optical flow and interpolation kernels to synthesize Target Frame pixels. This layer is fully differentiable such that both the flow and kernel estimation networks can be optimized jointly. The proposed model benefits from the advantages of motion estimation and compensation methods without using hand-crafted features. Compared to existing methods, our approach is computationally efficient and able to generate more visually appealing results. Furthermore, the proposed MEMC-Net architecture can be seamlessly adapted to several video enhancement tasks, e.g., super-resolution, denoising, and deblocking. Extensive quantitative and qualitative evaluations demonstrate that the proposed method performs favorably against the state-of-the-art video Frame interpolation and enhancement algorithms on a wide range of datasets.

  • memc net motion estimation and motion compensation driven neural network for video interpolation and enhancement
    arXiv: Computer Vision and Pattern Recognition, 2018
    Co-Authors: Wenbo Bao, Weisheng Lai, Xiaoyun Zhang, Zhiyong Gao, Minghsuan Yang
    Abstract:

    Motion estimation (ME) and motion compensation (MC) have been widely used for classical video Frame interpolation systems over the past decades. Recently, a number of data-driven Frame interpolation methods based on convolutional neural networks have been proposed. However, existing learning based methods typically estimate either flow or compensation kernels, thereby limiting performance on both computational efficiency and interpolation accuracy. In this work, we propose a motion estimation and compensation driven neural network for video Frame interpolation. A novel adaptive warping layer is developed to integrate both optical flow and interpolation kernels to synthesize Target Frame pixels. This layer is fully differentiable such that both the flow and kernel estimation networks can be optimized jointly. The proposed model benefits from the advantages of motion estimation and compensation methods without using hand-crafted features. Compared to existing methods, our approach is computationally efficient and able to generate more visually appealing results. Furthermore, the proposed MEMC-Net can be seamlessly adapted to several video enhancement tasks, e.g., super-resolution, denoising, and deblocking. Extensive quantitative and qualitative evaluations demonstrate that the proposed method performs favorably against the state-of-the-art video Frame interpolation and enhancement algorithms on a wide range of datasets.

Zhiyong Gao - One of the best experts on this subject based on the ideXlab platform.

  • memc net motion estimation and motion compensation driven neural network for video interpolation and enhancement
    IEEE Transactions on Pattern Analysis and Machine Intelligence, 2021
    Co-Authors: Wenbo Bao, Weisheng Lai, Xiaoyun Zhang, Zhiyong Gao, Minghsuan Yang
    Abstract:

    Motion estimation (ME) and motion compensation (MC) have been widely used for classical video Frame interpolation systems over the past decades. Recently, a number of data-driven Frame interpolation methods based on convolutional neural networks have been proposed. However, existing learning based methods typically estimate either flow or compensation kernels, thereby limiting performance on both computational efficiency and interpolation accuracy. In this work, we propose a motion estimation and compensation driven neural network for video Frame interpolation. A novel adaptive warping layer is developed to integrate both optical flow and interpolation kernels to synthesize Target Frame pixels. This layer is fully differentiable such that both the flow and kernel estimation networks can be optimized jointly. The proposed model benefits from the advantages of motion estimation and compensation methods without using hand-crafted features. Compared to existing methods, our approach is computationally efficient and able to generate more visually appealing results. Furthermore, the proposed MEMC-Net architecture can be seamlessly adapted to several video enhancement tasks, e.g., super-resolution, denoising, and deblocking. Extensive quantitative and qualitative evaluations demonstrate that the proposed method performs favorably against the state-of-the-art video Frame interpolation and enhancement algorithms on a wide range of datasets.

  • memc net motion estimation and motion compensation driven neural network for video interpolation and enhancement
    arXiv: Computer Vision and Pattern Recognition, 2018
    Co-Authors: Wenbo Bao, Weisheng Lai, Xiaoyun Zhang, Zhiyong Gao, Minghsuan Yang
    Abstract:

    Motion estimation (ME) and motion compensation (MC) have been widely used for classical video Frame interpolation systems over the past decades. Recently, a number of data-driven Frame interpolation methods based on convolutional neural networks have been proposed. However, existing learning based methods typically estimate either flow or compensation kernels, thereby limiting performance on both computational efficiency and interpolation accuracy. In this work, we propose a motion estimation and compensation driven neural network for video Frame interpolation. A novel adaptive warping layer is developed to integrate both optical flow and interpolation kernels to synthesize Target Frame pixels. This layer is fully differentiable such that both the flow and kernel estimation networks can be optimized jointly. The proposed model benefits from the advantages of motion estimation and compensation methods without using hand-crafted features. Compared to existing methods, our approach is computationally efficient and able to generate more visually appealing results. Furthermore, the proposed MEMC-Net can be seamlessly adapted to several video enhancement tasks, e.g., super-resolution, denoising, and deblocking. Extensive quantitative and qualitative evaluations demonstrate that the proposed method performs favorably against the state-of-the-art video Frame interpolation and enhancement algorithms on a wide range of datasets.

Xiaoyun Zhang - One of the best experts on this subject based on the ideXlab platform.

  • memc net motion estimation and motion compensation driven neural network for video interpolation and enhancement
    IEEE Transactions on Pattern Analysis and Machine Intelligence, 2021
    Co-Authors: Wenbo Bao, Weisheng Lai, Xiaoyun Zhang, Zhiyong Gao, Minghsuan Yang
    Abstract:

    Motion estimation (ME) and motion compensation (MC) have been widely used for classical video Frame interpolation systems over the past decades. Recently, a number of data-driven Frame interpolation methods based on convolutional neural networks have been proposed. However, existing learning based methods typically estimate either flow or compensation kernels, thereby limiting performance on both computational efficiency and interpolation accuracy. In this work, we propose a motion estimation and compensation driven neural network for video Frame interpolation. A novel adaptive warping layer is developed to integrate both optical flow and interpolation kernels to synthesize Target Frame pixels. This layer is fully differentiable such that both the flow and kernel estimation networks can be optimized jointly. The proposed model benefits from the advantages of motion estimation and compensation methods without using hand-crafted features. Compared to existing methods, our approach is computationally efficient and able to generate more visually appealing results. Furthermore, the proposed MEMC-Net architecture can be seamlessly adapted to several video enhancement tasks, e.g., super-resolution, denoising, and deblocking. Extensive quantitative and qualitative evaluations demonstrate that the proposed method performs favorably against the state-of-the-art video Frame interpolation and enhancement algorithms on a wide range of datasets.

  • memc net motion estimation and motion compensation driven neural network for video interpolation and enhancement
    arXiv: Computer Vision and Pattern Recognition, 2018
    Co-Authors: Wenbo Bao, Weisheng Lai, Xiaoyun Zhang, Zhiyong Gao, Minghsuan Yang
    Abstract:

    Motion estimation (ME) and motion compensation (MC) have been widely used for classical video Frame interpolation systems over the past decades. Recently, a number of data-driven Frame interpolation methods based on convolutional neural networks have been proposed. However, existing learning based methods typically estimate either flow or compensation kernels, thereby limiting performance on both computational efficiency and interpolation accuracy. In this work, we propose a motion estimation and compensation driven neural network for video Frame interpolation. A novel adaptive warping layer is developed to integrate both optical flow and interpolation kernels to synthesize Target Frame pixels. This layer is fully differentiable such that both the flow and kernel estimation networks can be optimized jointly. The proposed model benefits from the advantages of motion estimation and compensation methods without using hand-crafted features. Compared to existing methods, our approach is computationally efficient and able to generate more visually appealing results. Furthermore, the proposed MEMC-Net can be seamlessly adapted to several video enhancement tasks, e.g., super-resolution, denoising, and deblocking. Extensive quantitative and qualitative evaluations demonstrate that the proposed method performs favorably against the state-of-the-art video Frame interpolation and enhancement algorithms on a wide range of datasets.

Weisheng Lai - One of the best experts on this subject based on the ideXlab platform.

  • memc net motion estimation and motion compensation driven neural network for video interpolation and enhancement
    IEEE Transactions on Pattern Analysis and Machine Intelligence, 2021
    Co-Authors: Wenbo Bao, Weisheng Lai, Xiaoyun Zhang, Zhiyong Gao, Minghsuan Yang
    Abstract:

    Motion estimation (ME) and motion compensation (MC) have been widely used for classical video Frame interpolation systems over the past decades. Recently, a number of data-driven Frame interpolation methods based on convolutional neural networks have been proposed. However, existing learning based methods typically estimate either flow or compensation kernels, thereby limiting performance on both computational efficiency and interpolation accuracy. In this work, we propose a motion estimation and compensation driven neural network for video Frame interpolation. A novel adaptive warping layer is developed to integrate both optical flow and interpolation kernels to synthesize Target Frame pixels. This layer is fully differentiable such that both the flow and kernel estimation networks can be optimized jointly. The proposed model benefits from the advantages of motion estimation and compensation methods without using hand-crafted features. Compared to existing methods, our approach is computationally efficient and able to generate more visually appealing results. Furthermore, the proposed MEMC-Net architecture can be seamlessly adapted to several video enhancement tasks, e.g., super-resolution, denoising, and deblocking. Extensive quantitative and qualitative evaluations demonstrate that the proposed method performs favorably against the state-of-the-art video Frame interpolation and enhancement algorithms on a wide range of datasets.

  • memc net motion estimation and motion compensation driven neural network for video interpolation and enhancement
    arXiv: Computer Vision and Pattern Recognition, 2018
    Co-Authors: Wenbo Bao, Weisheng Lai, Xiaoyun Zhang, Zhiyong Gao, Minghsuan Yang
    Abstract:

    Motion estimation (ME) and motion compensation (MC) have been widely used for classical video Frame interpolation systems over the past decades. Recently, a number of data-driven Frame interpolation methods based on convolutional neural networks have been proposed. However, existing learning based methods typically estimate either flow or compensation kernels, thereby limiting performance on both computational efficiency and interpolation accuracy. In this work, we propose a motion estimation and compensation driven neural network for video Frame interpolation. A novel adaptive warping layer is developed to integrate both optical flow and interpolation kernels to synthesize Target Frame pixels. This layer is fully differentiable such that both the flow and kernel estimation networks can be optimized jointly. The proposed model benefits from the advantages of motion estimation and compensation methods without using hand-crafted features. Compared to existing methods, our approach is computationally efficient and able to generate more visually appealing results. Furthermore, the proposed MEMC-Net can be seamlessly adapted to several video enhancement tasks, e.g., super-resolution, denoising, and deblocking. Extensive quantitative and qualitative evaluations demonstrate that the proposed method performs favorably against the state-of-the-art video Frame interpolation and enhancement algorithms on a wide range of datasets.