Dual-stream mutually adaptive quality assessment for authentic distortion image

Abstract

To address semantic misperception caused by distortion and accurately simulate human perceptual processes, this study proposed a method that utilised dual-stream mutually adaptive feature mapping. To extract complementary features, a dual-stream unsupervised method was used during the training phase. One stream was responsible for the extraction of low-level and global features, whereas the other was dedicated to extracting high-level semantic and positional features. Following the freezing of feature extraction network weights, we proposed a structure that used standard deviation labels to predict the quality distribution. The experimental results obtained from 10 published image quality databases demonstrated the superiority of the proposed algorithm. The algorithm outperformed the majority of mainstream methods when evaluated on authentic distortion databases and exhibited competitive performance on synthetic distortion databases.