Fei Gao

TL;DR: This paper develops a novel deep multimodal distance metric learning (Deep-MDML) method, which adopts a new ranking model to use multi-modal features, including click features and visual features in DML.

TL;DR: Thorough experiments conducted on standard databases show that the proposed novel full-reference IQA framework, codenamed DeepSim, can accurately predict human perceived image quality and outperforms previous state-of-the-art performance.

TL;DR: Zhang et al. as discussed by the authors explored and exploited preference image pairs (PIPs) such as the quality of image I is better than image B for training a robust blind image quality assessment (BIQA) model.

TL;DR: Two universal blind quality assessment models are presented, NSS global scheme and NSS two-step scheme, which are in remarkably high consistency with the human perception, and overwhelm representative universal blind algorithms as well as some standard full reference quality indexes.

TL;DR: Thorough experiments have been conducted on five standard databases, which show that a significant improvement can be achieved by adopting multi-level deep representations from a very deep DNN model for learning an effective BIQA model, and consequently BLINDER considerably outperforms previous state-of-the-art BIZA methods for authentically distorted images.