Adversarial Video Compression Guided by Soft Edge Detection
arXiv:1811.10673
Abstract
We propose a video compression framework using conditional Generative Adversarial Networks (GANs). We rely on two encoders: one that deploys a standard video codec and another which generates low-level maps via a pipeline of down-sampling, a newly devised soft edge detector, and a novel lossless compression scheme. For decoding, we use a standard video decoder as well as a neural network based one, which is trained using a conditional GAN. Recent "deep" approaches to video compression require multiple videos to pre-train generative networks to conduct interpolation. In contrast to this prior work, our scheme trains a generative decoder on pairs of a very limited number of key frames taken from a single video and corresponding low-level maps. The trained decoder produces reconstructed frames relying on a guidance of low-level maps, without any interpolation. Experiments on a diverse set of 131 videos demonstrate that our proposed GAN-based compression engine achieves much higher quality reconstructions at very low bitrates than prevailing standard codecs such as H.264 or HEVC.
References in corpus (12)
- Conditional Generative Adversarial Nets
- End-to-end Optimized Image Compression
- Generating Videos with Scene Dynamics
- Deep Predictive Coding Networks for Video Prediction and Unsupervised Learning
- Stochastic Video Generation with a Learned Prior
- Video (language) modeling: a baseline for generative models of natural videos
- Unsupervised Learning for Physical Interaction through Video Prediction
- Lossy Image Compression with Compressive Autoencoders
- Spatio-temporal video autoencoder with differentiable memory
- Stochastic Adversarial Video Prediction
- Learning to Generate Images of Outdoor Scenes from Attributes and Semantic Layouts
- Video Compression through Image Interpolation