Fast-MFQE: A Fast Approach for Multi-Frame Quality Enhancement on Compressed Video

Fast-MFQE: A Fast Approach for Multi-Frame Quality Enhancement on Compressed Video

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For compressed images and videos, quality enhancement is essential.Though there have been remarkable achievements related to deep learning, deep From Life-Skills Research and Training to Sustainability: A Case Study from a Spanish University learning models are too large to apply to real-time tasks.Therefore, a fast multi-frame quality enhancement method for compressed video, named Fast-MFQE, is proposed to meet the requirement of video-quality enhancement for real-time applications.

There are three main modules in this method.One is the image pre-processing building module (IPPB), which is used to reduce redundant information of input images.The second one is the spatio-temporal fusion attention (STFA) module.

It is introduced to effectively merge temporal and spatial information of input video frames.The third one is the Linear Dunes: Morphology from Google Earth feature reconstruction network (FRN), which is developed to effectively reconstruct and enhance the spatio-temporal information.Experimental results demonstrate that the proposed method outperforms state-of-the-art methods in terms of lightweight parameters, inference speed, and quality enhancement performance.

Even at a resolution of 1080p, the Fast-MFQE achieves a remarkable inference speed of over 25 frames per second, while providing a PSNR increase of 19.6% on average when QP = 37.