Innovative Approaches in Image Quality Assessment: A Deep Learning-Enabled Multi Level and Multi-Scale Perspective

In the dynamic field of digital image technology, the imperative role of Image Quality Assessment (IQA) is increasingly recognized. Traditional methodologies, designed to echo human visual processing, frequently encounter challenges in diverse application landscapes, primarily due to their singular focus on limited scale and level analysis. This shortcoming curtails their efficacy in practical scenarios. The incorporation of deep learning paradigms into IQA has notably enhanced evaluation capabilities. Yet, there remains a scope for refinement, especially in areas like integrating multi-scale data, fusing features at multiple levels, and optimizing computational resources. Addressing these gaps, this study proposes an advanced multi-level and multi-scale IQA strategy, harnessing the power of deep learning. A unique end-to-end multi-scale IQA module has been crafted, tailored to aggregate image quality data across a spectrum of scales comprehensively. Additionally, this research introduces an IQA model built upon the foundation of multi-level feature fusion. This innovative model stands out in its capacity to efficiently assess image quality, by adeptly extracting and amalgamating features from various levels. Beyond enhancing accuracy in quality scoring, this approach significantly bolsters the model's interpretability and operational efficiency, marking a stride forward in digital image processing research and applications.