We extend the DISTS index to a locally adaptive version – A-DISTS, which separates structure and texture across space and scale, and assign the adaptive weighting of quality measurements according to local image content.

We performed a large-scale comparison of full-reference IQA models in terms of their use as objectives for the optimization of image processing algorithms — denoising, deblurring, super-resolution, and compression.

We proposed a Deep Image Structure and Texture Similarity (DISTS) metric, which is sensitive to structural distortions, tolerant of texture resampling, and robust to mild geometric transformations.

We conducted a systematic study of intrinsic image popularity assessment on social media, and constructed a large-scale image database, and proposed a DNN-based computational model for popularity prediction.

We presented a social media popularity prediction strategy based on multiple features fusion with deep neural network for ACM Multimedia Challenge.