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A Hardware-Aware and Content-Adaptive Super Resolution System for Bandwidth-Limited IoT Video Delivery

Year: 2024

Type: conference-paper

Abstract: The demand for video delivery in the Internet of Things (IoT) is increasing.Limited Internet bandwidth is under immense pressure and struggles to meet the requirements for low latency.This paper proposes a low-bandwidth IoT video delivery solution that transmits low-resolution videos over the Internet and uses superresolution (SR) technology at the edge to upscale the videos and restore their quality.Firstly, a hardware-aware super-resolution (SR) neural architecture search (NAS) framework is proposed, which considers the latency of the target hardware to design super-resolution algorithms automatically, based on the observation that latency does not grow continuously with the amount of computation in the algorithm.This framework yields two super-resolution algorithms with different upscaling capabilities and latency.Additionally, a content-adaptive task scheduling scheme is introduced, which selects one of the two super-resolution algorithms for upscaling each image block based on the block's upscaling difficulty.Lastly, a heterogenous-multithreading deployment scheme is presented, which leverages the heterogeneous computing resources in the targeted hardware to parallelize the execution of various algorithm modules in the super-resolution system.The proposed superresolution system can achieve up to a 1.8× speedup.

Source: International Conference on the Internet of Things (IoT)

DOI: 10.1145/3703790.3703793

Authors: Xuyang Duan, Zikang Zhou, Wu Qiu, Zhiyi Shu, Zheng Tang, Jun Han

Institution: Fudan University

Language: English

Topic: Advanced Image Processing Techniques

Subfield: Computer Vision and Pattern Recognition

Field: Computer Science

Domain: Physical Sciences

SDG: Industry, innovation and infrastructure

Open Access status: bronze

Funder: National Natural Science Foundation of China