Abstract
Video frame interpolation fuse several low-resolution (LR) frames into one high-resolution (HR) frame. The existing methods for video frame interpolation use optical flow method to determine motion in a scene, but computation using optical flow method is difficult, which can lead to artifacts in the output video. In many applications where we use video footages, there is a similarity in the content of footages. This similarity in content recommends that using some kind of context-aware approach can do better interpolation than the different existing interpolation techniques. We propose such a context-aware approach for video interpolation, the video frame interpolation using convolutional neural networks. In this proposed method, neighboring images are given as input to an end-to-end convolutional neural network which interpolates a frame between them. A comparative analysis of video interpolation technique using proposed RGB model and HSV model using metric standards such as SSIM, PSNR, and MSE is also included in the proposed method.
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References
Kang K, Ouyang W, Li H, Wang X (2016) Object detection from video tubelets with convolutional neural networks. In: IEEE conference on computer vision and pattern recognition (CVPR), pp 817–825
Dong C, Loy CC, He K, Tang X (2016) Image super-resolution using deep convolutional networks. IEEE Trans Pattern Anal Mach Intell 38(2):295307
Hung K-W, Siu W-C (2011) Fast video interpolation/upsampling using linear motion model. In: 18th IEEE international conference on image processing
Guo D, Lu Z (2016) Motion-compensated Frame Interpolation with weighted motion estimation and hierarchical vector refinement. Neurocomputing 181
Ghutke RC, Naveen C, Satpute VR (2016) Temporal video frame interpolation using new cubic motion compensation technique. In: IEEE international conference on signal processing and communications (SPCOM)
Saravanan G, Yamuna G, Nandhini S (2016) Real time implementation of RGB to HSV/HSI/HSL and its reverse color space models. In: IEEE international conference on communication and signal processing, 6–8 April 2016
Kingma DP, Ba J (2014) Adam: a method for stochastic optimization. CoRR abs/1412.6980 available at http://arxiv.org/abs/1412.6980
Ronneberger O, Fischer P, Brox T (2015) U-Net: convolutional networks for biomedical image segmentation, CoRR abs/1505.04597, available at http://arxiv.org/abs/1505.04597
Song BC, Jeong SC, Choi Y (2011) Video super-resolution algorithm using bi-directional overlapped block motion compensation and on-the-fly dictionary training. IEEE Trans Circuits Syst Video Technol 21(3):274–285
Kotevski Z, Mitrevski P (2009) Experimental comparison of PSNR and SSIM metrics for video quality estimation. In: Davcev D, Gmez JM (eds) ICT innovations. Springer, Berlin, Heidelberg
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Mathai, V., Baby, A., Sabu, A., Jose, J., Kuriakose, B. (2019). Video Frame Interpolation Using Deep Convolutional Neural Network. In: Pandian, D., Fernando, X., Baig, Z., Shi, F. (eds) Proceedings of the International Conference on ISMAC in Computational Vision and Bio-Engineering 2018 (ISMAC-CVB). ISMAC 2018. Lecture Notes in Computational Vision and Biomechanics, vol 30. Springer, Cham. https://doi.org/10.1007/978-3-030-00665-5_82
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DOI: https://doi.org/10.1007/978-3-030-00665-5_82
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