Open Access

ARTICLE

A Unified Model Fusing Region of Interest Detection and Super Resolution for Video Compression

by Xinkun Tang^1,2, Feng Ouyang^1,2, Ying Xu^2,*, Ligu Zhu¹, Bo Peng¹

1 School of Computer and Cyber Sciences, Communication University of China, Beijing, 100024, China
2 Cable Television Technology Research Institute, Academy of Broadcasting Science, Beijing, 100866, China

* Corresponding Author: Ying Xu. Email:

(This article belongs to the Special Issue: Edge Computing in Advancing the Capabilities of Smart Cities)

Computers, Materials & Continua 2024, 79(3), 3955-3975. https://doi.org/10.32604/cmc.2024.049057

Received 26 December 2023; Accepted 12 March 2024; Issue published 20 June 2024

Abstract

High-resolution video transmission requires a substantial amount of bandwidth. In this paper, we present a novel video processing methodology that innovatively integrates region of interest (ROI) identification and super-resolution enhancement. Our method commences with the accurate detection of ROIs within video sequences, followed by the application of advanced super-resolution techniques to these areas, thereby preserving visual quality while economizing on data transmission. To validate and benchmark our approach, we have curated a new gaming dataset tailored to evaluate the effectiveness of ROI-based super-resolution in practical applications. The proposed model architecture leverages the transformer network framework, guided by a carefully designed multi-task loss function, which facilitates concurrent learning and execution of both ROI identification and resolution enhancement tasks. This unified deep learning model exhibits remarkable performance in achieving super-resolution on our custom dataset. The implications of this research extend to optimizing low-bitrate video streaming scenarios. By selectively enhancing the resolution of critical regions in videos, our solution enables high-quality video delivery under constrained bandwidth conditions. Empirical results demonstrate a 15% reduction in transmission bandwidth compared to traditional super-resolution based compression methods, without any perceivable decline in visual quality. This work thus contributes to the advancement of video compression and enhancement technologies, offering an effective strategy for improving digital media delivery efficiency and user experience, especially in bandwidth-limited environments. The innovative integration of ROI identification and super-resolution presents promising avenues for future research and development in adaptive and intelligent video communication systems.

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Keywords

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