High-Frequency Semantic Enhancement in Compressed Scenarios for Robust Visual and Machine Vision Applications

With the growing demand for video processing in both human and machine vision, optimizing post-processing techniques has become a crucial challenge. To address the limitations of current post-processing techniques in these domains, this paper introduces a novel post-processing method that enhances high-frequency information through semantic enhancement, significantly improving performance in both domains. We propose a High Semantic Extraction (HSE) model to capture more recognizable details, and design a High-Frequency Semantic Fusion (HFSF) strategy that preserves critical details while suppressing noise. Experimental results demonstrate that our method effectively enhances performance in object detection, semantic segmentation, and video quality, achieving a significant advancement in optimizing video processing for both human and machine vision.