Dynamic Frame Interpolation in Wavelet Domain

TL;DR

WaveletVFI introduces a two-stage, wavelet-based frame interpolation method that adaptively reduces computation by learning dynamic thresholds, achieving high efficiency with minimal accuracy loss.

Contribution

The paper presents a novel wavelet domain interpolation framework with a dynamic threshold mechanism for computation reduction, outperforming existing methods in efficiency.

Findings

Reduces computation by up to 40% on benchmarks.

Maintains similar accuracy to state-of-the-art methods.

Employs a dynamic threshold learned via a classifier for adaptive computation.

Abstract

Video frame interpolation is an important low-level vision task, which can increase frame rate for more fluent visual experience. Existing methods have achieved great success by employing advanced motion models and synthesis networks. However, the spatial redundancy when synthesizing the target frame has not been fully explored, that can result in lots of inefficient computation. On the other hand, the computation compression degree in frame interpolation is highly dependent on both texture distribution and scene motion, which demands to understand the spatial-temporal information of each input frame pair for a better compression degree selection. In this work, we propose a novel two-stage frame interpolation framework termed WaveletVFI to address above problems. It first estimates intermediate optical flow with a lightweight motion perception network, and then a wavelet synthesis network uses flow aligned context features to predict multi-scale wavelet coefficients with sparse convolution for efficient target frame reconstruction, where the sparse valid masks that control computation in each scale are determined by a crucial threshold ratio. Instead of setting a fixed value like previous methods, we find that embedding a classifier in the motion perception network to learn a dynamic threshold for each sample can achieve more computation reduction with almost no loss of accuracy. On the common high resolution and animation frame interpolation benchmarks, proposed WaveletVFI can reduce computation up to 40% while maintaining similar accuracy, making it perform more efficiently against other state-of-the-arts. Code is available at https://github.com/ltkong218/WaveletVFI.