Search Results for author:
Found 6 papers, 1 papers with code
PIM: Video Coding using Perceptual Importance Maps
The contributions of this work are threefold: (1) we introduce a web-tool which allows scalable collection of fine-grained perceptual importance, by having users interactively paint spatio-temporal maps over encoded videos; (2) we use this tool to collect a dataset with 178 videos with a total of 14443 frames of human annotated spatio-temporal importance maps over the videos; and (3) we use our curated dataset to train a lightweight machine learning model which can predict these spatio-temporal importance regions.
An Interactive Annotation Tool for Perceptual Video Compression
We use this tool to collect data in-the-wild (10 videos, 17 users) and utilize the obtained importance maps in the context of x264 coding to demonstrate that the tool can indeed be used to generate videos which, at the same bitrate, look perceptually better through a subjective study - and are 1. 9 times more likely to be preferred by viewers.
ELF-VC: Efficient Learned Flexible-Rate Video Coding
In this setting, for natural videos our approach compares favorably across the entire R-D curve under metrics PSNR, MS-SSIM and VMAF against all mainstream video standards (H. 264, H. 265, AV1) and all ML codecs.
Learned Video Compression
We present a new algorithm for video coding, learned end-to-end for the low-latency mode.
The High-Dimensional Geometry of Binary Neural Networks
However, there is a dearth of theoretical analysis to explain why we can effectively capture the features in our data with binary weights and activations.
DeepMovie: Using Optical Flow and Deep Neural Networks to Stylize Movies
The other naive method that initializes the optimization for the next frame using the rendered version of the previous frame also produces poor results because the features of the texture stay fixed relative to the frame of the movie instead of moving with objects in the scene.
