MEMC-Net: Motion Estimation and Motion Compensation Driven Neural Network for Video Interpolation and Enhancement

TL;DR

MEMC-Net introduces a neural network for video frame interpolation that combines motion estimation and compensation with an adaptive warping layer, achieving higher accuracy and efficiency than previous methods, and adaptable to various video enhancement tasks.

Contribution

The paper presents a novel neural network architecture that jointly estimates optical flow and interpolation kernels using a differentiable adaptive warping layer, improving performance and efficiency.

Findings

Outperforms state-of-the-art interpolation methods on multiple datasets.

Achieves higher visual quality with reduced computational cost.

Successfully adapts to super-resolution, denoising, and deblocking tasks.

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.