Nested Error Map Generation Network for No-Reference Image Quality Assessment

Abstract

We propose a multi-task learning neural network for No-Reference image quality assessment (NR-IQA). The pro-posed architecture consists of a backbone feature extractor, a nested multi-task generative module and a quality regression module. We adopt a coarse-to-fine strategy to predict objective error maps in two subtasks optimized with different loss functions. The network is designed to be nested such that discriminative features learned from subtasks are efficiently shared by the primary task. Perceptual distortion maps are achieved by applying masking mechanism between reconstructed error maps and the learned distortion sensitivity map. At last, a quality regression module is adopted to nonlinearly map masked distortions to the subjective score. Experimental results demonstrate the superior performances of the proposed model over state-of-the-art models. The implementation of our method is released at https://github.com/R-JunmingChen/NEMG-IQA.