Blind Image Quality Assessment Using Multi-Stream Architecture with Spatial and Channel Attention

TL;DR

This paper introduces a multi-stream spatial and channel attention-based algorithm for blind image quality assessment that emphasizes regions of interest, achieving high correlation with human perception across various datasets.

Contribution

It proposes a novel multi-stream architecture with spatial and channel attention to improve accuracy in blind image quality assessment, focusing on perceptual foreground regions.

Findings

High correlation with human perceptual scores

Effective on both synthetic and authentic distortions

Demonstrates strong generalization across datasets

Abstract

BIQA (Blind Image Quality Assessment) is an important field of study that evaluates images automatically. Although significant progress has been made, blind image quality assessment remains a difficult task since images vary in content and distortions. Most algorithms generate quality without emphasizing the important region of interest. In order to solve this, a multi-stream spatial and channel attention-based algorithm is being proposed. This algorithm generates more accurate predictions with a high correlation to human perceptual assessment by combining hybrid features from two different backbones, followed by spatial and channel attention to provide high weights to the region of interest. Four legacy image quality assessment datasets are used to validate the effectiveness of our proposed approach. Authentic and synthetic distortion image databases are used to demonstrate the effectiveness of the proposed method, and we show that it has excellent generalization properties with a particular focus on the perceptual foreground information.