Zhou Wang

TL;DR: A gradient ascent-based algorithm, which starts from any initial point in the space of all possible images and iteratively moves towards the direction that improves MEF-SSIM until convergence, and the final high quality fused image appears to have little dependence on the initial image.

TL;DR: This work introduces the Smartphone Photography Attribute and Quality (SPAQ) database, consisting of 11,125 pictures taken by 66 smartphones, where each image is attached with so far the richest annotations, and makes the first attempts to train blind image quality assessment (BIQA) models constructed by baseline and multi-task deep neural networks.

TL;DR: This work builds a streaming video database and carries out a subjective user study to investigate the human responses to the combined effect of video compression, initial buffering, and stalling, and proposes a novel QoE prediction approach named Streaming QOE Index that accounts for the instantaneous quality degradation due to perceptual video presentation impairment, the playback stalling events, and the instantaneous interactions between them.

TL;DR: An efficient methodology for comparing computational models of a perceptually discriminable quantity is proposed, which first synthesizes a pair of stimuli that maximize/minimize the response of one model while holding the other fixed.

TL;DR: A binocular rivalry-inspired multi-scale model to predict the quality of stereoscopic images from that of the single-view images is proposed, and the results show that the proposed model successfully eliminates the prediction bias, leading to significantly improved quality prediction of the stereoscope images.