Learning for Video Compression

Understand

One key challenge to learning-based video compression is that motion predictive coding, a very effective tool for video compression, can hardly be trained into a neural network.

• In this paper we propose the concept of PixelMotionCNN (PMCNN) which includes motion extension and hybrid prediction networks.
• PMCNN can model spatiotemporal coherence to effectively perform predictive coding inside the learning network.
• On the basis of PMCNN, we further explore a learning-based framework for video compression with additional components of iterative analysis/synthesis, binarization, etc.