Zhengfang Duanmu

TL;DR: This work establishes a large-scale database named the Waterloo Exploration Database, which in its current state contains 4744 pristine natural images and 94 880 distorted images created from them, and presents three alternative test criteria to evaluate the performance of IQA models, namely, the pristine/distorted image discriminability test, the listwise ranking consistency test, and the pairwise preference consistency test.

TL;DR: This work demonstrates the strong competitiveness of MEON against state-of-the-art BIQA models using the group maximum differentiation competition methodology and empirically demonstrates that GDN is effective at reducing model parameters/layers while achieving similar quality prediction performance.

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 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: Across an independent set of test sequences, it is found that the optimized MEF-Net achieves consistent improvement in visual quality for most sequences, and runs 10 to 1000 times faster than state-of-the-art methods.