LiNuIQA: Lightweight No-Reference Image Quality Assessment Based on Non-Uniform Weighting

Abstract

No-Reference Image Quality Assessment (NR-IQA) techniques have shown improved performance with the help of deep-learning but lightweight architectures have not received attention. In this paper, we propose an NR-IQA network named Lightweight Non-uniform Weighting-based NR-IQA (LiNuIQA) that adopts an efficient network as a feature extractor for a resource constraint environment and harnesses non-uniformly self-weighted local (from each patch) and global information (from all patches) to overcome the inherent problem of low performance stemming from use of lightweight feature extractor. This non-uniform weighting technique is designed to utilize combinations of local and global information with very low resources unlike conventional weighting techniques. The experimental results show that our network outperforms several recently popular NR-IQA networks in terms of both PLCC and SRCC while having the smallest number of parameters and multiply-adds (MAdd) operations. In addition, it can be seen from our experiments that appropriate weighting method plays an important role in IQA and can be implemented with extremely low resources.