NIO: Lightweight neural operator-based architecture for video frame interpolation

• URL: http://arxiv.org/abs/2211.10791v1
• Date: Sat, 19 Nov 2022 20:30:47 GMT
• Title: NIO: Lightweight neural operator-based architecture for video frame interpolation
• Authors: Hrishikesh Viswanath, Md Ashiqur Rahman, Rashmi Bhaskara, Aniket Bera
• Abstract summary: NIO is a lightweight, efficient neural operator-based architecture to perform video frame-by-frame learning. We show that NIO can produce visually-smooth and accurate results and converges in fewer epochs than state-of-the-art approaches.
• Score: 15.875579519177487
• License: http://creativecommons.org/publicdomain/zero/1.0/
• Abstract: We present, NIO - Neural Interpolation Operator, a lightweight efficient neural operator-based architecture to perform video frame interpolation. Current deep learning based methods rely on local convolutions for feature learning and require a large amount of training on comprehensive datasets. Furthermore, transformer-based architectures are large and need dedicated GPUs for training. On the other hand, NIO, our neural operator-based approach learns the features in the frames by translating the image matrix into the Fourier space by using Fast Fourier Transform (FFT). The model performs global convolution, making it discretization invariant. We show that NIO can produce visually-smooth and accurate results and converges in fewer epochs than state-of-the-art approaches. To evaluate the visual quality of our interpolated frames, we calculate the structural similarity index (SSIM) and Peak Signal to Noise Ratio (PSNR) between the generated frame and the ground truth frame. We provide the quantitative performance of our model on Vimeo-90K dataset, DAVIS, UCF101 and DISFA+ dataset.

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