2026-09-21T11:37:20Z https://www.ideals.illinois.edu/oai-pmh

oai:www.ideals.illinois.edu:2142/115801 2023-07-11 col_2142_5131 col_2142_8888 com_2142_5130 com_2142_8887 com_2142_234

Patel, Sanjay 2022-05 application/pdf en text Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2022-11-11 without embargo terms The student, Sanchit Vohra, accepted the attached license on 2022-04-28 at 14:55. The student, Sanchit Vohra, submitted this Thesis for approval on 2022-04-28 at 14:55. This Thesis was approved for publication on 2022-04-28 at 16:17. DSpace SAF Submission Ingestion Package generated from Vireo submission #18002 on 2022-11-11 at 17:54:10 Streaming low-bandwidth real-time video using video super-resolution Vohra, Sanchit 2022-04-28 video compression H264 super-resolution super resolution video super-resolution real-time streaming teleoperations autonomy Jalapeño is a real-time video streaming platform designed primarily for tele-operations. The platform uses a traditional video compression approach (H264) and pairs that with unique networking optimizations and artificial super-scaling to increase reliability and decrease bandwidth consumption. Jalapeño assumes the vehicle (client) is running the platform on a device with low computational capabilities. Conversely, the operator is controlling the vehicle on a system with very high resources. Additionally, the client network is assumed to be transmitting over a lossy wireless channel. Real-time streaming also has tighter constraints than traditional live-streaming, hence some networking algorithms that rely on introducing artificial stream delays are infeasible. Jalapeño must alleviate the issues of video streaming under heavy loss conditions with a low-powered client under the challenging real-time constraints. The networking layer closely monitors the quality of the stream and adjusts the parameters of the compression and transmission dynamically. We leverage the temporal scalable video codec capabilities of the open-source OpenH264 codec by Cisco to design a reconstruction algorithm that works under real-time constraints to mitigate packet loss. The prime feature is video super-resolution, a GAN based generator that is able to upscale a low-resolution stream from the client to high-definition frames while maintaining temporal consistency. This thesis also outlines an approach to build a large video dataset for unsupervised video learning and discusses compression-aware super-resolution to advance the framework to the next level. Thesis eng https://hdl.handle.net/2142/115801 Copyright 2022 Sanchit Vohra M.S. Thesis Electrical & Computer Engr University of Illinois at Urbana-Champaign Electrical & Computer Eng