Removing Blocking Artifacts in Video Streams Using Event Cameras
This addresses video quality issues for applications under bandwidth constraints, but it is incremental as it builds on existing methods with simulated data.
The paper tackles the problem of removing blocking artifacts in video streams by proposing EveRestNet, a convolutional neural network that uses simulated neuromorphic sensor events alongside degraded video frames, resulting in improved image quality.
In this paper, we propose EveRestNet, a convolutional neural network designed to remove blocking artifacts in videostreams using events from neuromorphic sensors. We first degrade the video frame using a quadtree structure to produce the blocking artifacts to simulate transmitting a video under a heavily constrained bandwidth. Events from the neuromorphic sensor are also simulated, but are transmitted in full. Using the distorted frames and the event stream, EveRestNet is able to improve the image quality.