Video Super-resolution for Low Bitrate Streams

Abstract: Presenting a novel model for the joint problems of video super-resolution, removing compression artifacts and overall video enhancement of low-bitrate streams and combining Generative Adversarial Networks with Dynamic Upsampling Filters and a novel progressive training strategy that uses perceptual metrics. Derive real-time models for the cloud and evaluate on publicly available high-resolution datasets.

Bio: Roland Miezianko is a Senior Applied Scientist and Technical Lead at Amazon Grand Challenge, working on computer vision and machine learning models. His projects include the Amazon Glow communication device, Amazon Comprehend Medical NLP medical document entity extraction service, Amazon Care healthcare telepresence, medical knowledge graphs and chatbots, low-level computer vision detection using IR and EO sensors, with model deployment on edge devices and in the cloud. Roland received his Ph.D. from Temple University, where he focused on artificial intelligence software systems and video-based analytics. He received a Master’s degree in CIS from La Salle University and BS in Electrical Engineering from Boston University.