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

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.