Unifying Statistical and Refined Semantic Features for Lightweight No-Reference Image Quality Assessment

Abstract

No-reference image quality assessment (NR-IQA) is an important task of computer vision. Most deep neural networks based NR-IQA methods have the ability of accurate quality predictions, but they have large-scale parameters and high computational complexity. To alleviate these problems, we propose a lightweight NR-IQA method by unifying statistical and refined semantic features. Our proposed method consists of a lightweight feature extractor (LFE), a statistical semantic feature extraction (SSFE) module, and a refined semantic feature extraction (RSFE) module. The LFE is used to extract semantic features with perceptual distortion information. The SSFE module is designed to obtain statistical information of the semantic features for capturing the local and global changes of distorted image. The RSFE module is designed to refine the semantic features for measuring complex distortions. Extensive experiments on many image quality assessment (IQA) datasets are done and the results indicate that our proposed method outperforms some baseline NR-IQA methods in IQA performance, generalization ability, and model complexity.