Qing Ding

TL;DR: Wang et al. as discussed by the authors proposed a patch-wise spatial-temporal quality enhancement network which firstly extracts spatial and temporal features, then recalibrates and fuses the obtained spatial and time features.

TL;DR: A novel no-reference (NR) IQA model based on the convolutional neural network (CNN) for assessing the perceptual quality of SCIs is proposed and Experimental results verify that the model outperforms all test NR IQA methods and most FRIQA methods on the screen content image quality assessment database (SIQAD).

TL;DR: A novel VRF generation convolutional neural network (VRFCNN) to jointly handle enhancement of corresponding BL frame and compensation of previous EL frame is proposed, which outperforms other methods.

TL;DR: A novel blind quality enhancement framework for compressed video (BQEV), which utilizes a single network to conduct enhancement on videos compressed at various and unknown quality parameters (QPs), outperforming state-of-the-art approaches.