  Most deep learning methods for video frame interpolation consist of three
main components: feature extraction, motion estimation, and image synthesis.
Existing approaches are mainly distinguishable in terms of how these modules
are designed. However, when interpolating high-resolution images, e.g. at 4K,
the design choices for achieving high accuracy within reasonable memory
requirements are limited. The feature extraction layers help to compress the
input and extract relevant information for the latter stages, such as motion
estimation. However, these layers are often costly in parameters, computation
time, and memory. We show how ideas from dimensionality reduction combined with
a lightweight optimization can be used to compress the input representation
while keeping the extracted information suitable for frame interpolation.
Further, we require neither a pretrained flow network nor a synthesis network,
additionally reducing the number of trainable parameters and required memory.
When evaluating on three 4K benchmarks, we achieve state-of-the-art image
quality among the methods without pretrained flow while having the lowest
network complexity and memory requirements overall.
