Removing Blocking Artifacts in Video Streams Using Event Cameras

Abstract

In this paper, we propose EveRestNet, a convolutional neural network designed to remove blocking artifacts in video streams 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.

Adaptive and Efficient Hybrid In-loop Filter Based on Enhanced Generative Adversarial Networks with Sample Adaptive Offset Filter for HEVC/H-265

“…Any coding method that works on block-based prediction and transform coding produces discontinuity at block boundaries in the restoring signal [1]. Hence, the observable discontinuity at the block boundary is known as blocking artifacts [2]. Such blocking artifacts are arising based on coarse quantization method and block transform of error prediction method [3].…”