王新晟, 王密, 潘俊, 肖晶, 李路, 张昊楠, 赵简平. 珞珈三号01星视频智能插帧应用研究[J]. 武汉大学学报 ( 信息科学版), 2024, 49(6): 911-922. DOI: 10.13203/j.whugis20230439
引用本文: 王新晟, 王密, 潘俊, 肖晶, 李路, 张昊楠, 赵简平. 珞珈三号01星视频智能插帧应用研究[J]. 武汉大学学报 ( 信息科学版), 2024, 49(6): 911-922. DOI: 10.13203/j.whugis20230439
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WANG Xinsheng, WANG Mi, PAN Jun, XIAO Jing, LI Lu, ZHANG Haonan, ZHAO Jianping. Application of Intelligent Video Frame Interpolation for Luojia3-01 Satellite[J]. Geomatics and Information Science of Wuhan University, 2024, 49(6): 911-922. DOI: 10.13203/j.whugis20230439
Citation: WANG Xinsheng, WANG Mi, PAN Jun, XIAO Jing, LI Lu, ZHANG Haonan, ZHAO Jianping. Application of Intelligent Video Frame Interpolation for Luojia3-01 Satellite[J]. Geomatics and Information Science of Wuhan University, 2024, 49(6): 911-922. DOI: 10.13203/j.whugis20230439
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珞珈三号01星视频智能插帧应用研究

Application of Intelligent Video Frame Interpolation for Luojia3-01 Satellite

    摘要
  • 摘要: 光学视频卫星获取的高动态、高空间分辨率的数据为对地动态观测提供了新的技术手段。2023年初发射的珞珈三号01星是新一代智能测绘遥感科学试验卫星,该星可通过凝视成像模式获取对地高清彩色视频,但原始视频帧率较低,仅为6帧/s。为进一步提升珞珈三号01星视频的流畅度,降低视觉观感的卡顿效果,开展了面向珞珈三号01星视频插帧的相关研究。首先,针对卫星凝视成像过程中的误差进行了分析,提出了一种基于帧间透视变换模型的视频稳像方法,实现了原始视频数据的预处理；然后,考虑到日常可见光视频与卫星视频之间存在较大差异,且目前暂无可用的卫星视频插帧数据集,基于预处理后的稳像视频构建了一个涵盖不同场景的卫星插帧数据集Luojia3_VFISet；最后,基于无需光流模块参与的FLAVR (flow-agnostic video representation)视频插帧网络,通过将不同尺度的特征编码信息引入解码过程,提出了FLAVR_Plus视频插帧网络,进一步提升了卫星视频的插帧效果。实验结果表明,FLAVR_Plus插帧结果的测量峰值信噪比达到35.544 6 dB,精度提升约0.5%~7.2%,结构相似性可达0.917 9,同比提升约0.5%~8.7%。所构建的Luojia3_VFISet数据集有助于相关研究工作的开展,提出的FLAVR_Plus视频插帧网络针对不同场景均能生成质量良好、无明显拖影的中间帧,可有效提升珞珈三号01星视频的流畅度,为后续的卫星视频相关应用提供更多的帧间信息。

     

    Abstract:
    Objectives The dynamic and high spatial resolution data obtained by optical video satellites provide a new technical means for the dynamic observation of the earth. As a new generation of intelligent mapping remote sensing scientific test satellite, Luojia3-01 satellite was launched in early 2023, and it could acquire high-definition color video by gazing mode. However, the original video frame rate is low, only 6 frame per-second. To further improve the fluency and reduce the stuttering effect of visual perception, this paper carries out related research on intelligent video frame interpolation (VFI) of Luojia3-01 satellite video.
    Methods First, a satellite video image stabilization method based on the perspective transformation model is proposed after analyzing the imaging errors during the gaze mode. Second, satellite video is greatly different from daily video, and there is no available satellite VFI dataset at present. This paper builds a VFI dataset termed as Luojia3_VFISet based on the stabilized satellite videos, which covers different scenes. Finally, based on the flow-agnostic video representation (FLAVR) VFI network without optical flow module, the FLAVR_Plus VFI network is proposed to further improve the interpolation effect for satellite video by introducing the feature coding information of different scales from the encoder into the decoding process.
    Results The experimental results show that the interpolation of FLAVR_Plus VFI network raises the peak signal to noise ratio (PSNR) and structural similarity (SSIM) to 35.544 6 dB and 0.917 9, respectively, and it enhances the quality of synthesized intermediate frames with hardly-observed artifacts under different scenarios. Compared with other methods, the proposed network can improve the PSNR by 0.5% to 7.2% and the SSIM by 0.5% to 8.7%, respectively.
    Conclusions In this paper, the application of satellite VFI is studied by taking Luojia3-01 satellite as an example. The proposed Luojia3_VFISet dataset is conducive to the development of related research. The proposed FLAVR_Plus VFI network can effectively improve the fluency of the Loujia3-01 satellite video by generating interframes without artifacts and provide more interframe information for subsequent applications.

     

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