Abstract: No-reference image quality assessment (NR-IQA) aims to evaluate the perceptual quality of images without access to corresponding reference images and has broad applications in real-world image processing scenarios. However, existing NR-IQA methods often suffer from limited accuracy and generalization, especially under complex and diverse distortion types. To address this challenge, we propose Inc-LAENet, a novel NR-IQA framework that leverages multi-scale deep residual representations, integrates feature fusion mechanisms, and incorporates a local adaptive perception module to achieve improved assessment accuracy and generalization. Specifically, ResNet50 is employed to extract hierarchical residual features, an enhanced Inception-style module (Inc-s) strengthens sensitivity to various distortion patterns, and a lightweight local adaptive extraction module efficiently captures fine-grained structural information. Extensive experiments demonstrate the effectiveness of the proposed method, achieving SROCC values of 0.967 and 0.935 on the synthetic distortion datasets LIVE and CSIQ, and 0.852 and 0.898 on the authentic distortion datasets LIVEC and KonIQ-10k, respectively. These results confirm that Inc-LAENet provides a robust and efficient solution for NR-IQA tasks across both synthetic and real-world scenarios.

Minjuan GAO, Yankang LI and Xuande ZHANG, “Image Quality Assessment Based on Feature Fusion and Local Adaptation” International Journal of Advanced Computer Science and Applications(IJACSA), 16(6), 2025. http://dx.doi.org/10.14569/IJACSA.2025.0160651

@article{GAO2025,
title = {Image Quality Assessment Based on Feature Fusion and Local Adaptation},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2025.0160651},
url = {http://dx.doi.org/10.14569/IJACSA.2025.0160651},
year = {2025},
publisher = {The Science and Information Organization},
volume = {16},
number = {6},
author = {Minjuan GAO and Yankang LI and Xuande ZHANG}
}