IMAGE QUALITY ASSESSMENT: MODERN APPROACHES AND METRICS

Image quality assessment (IQA) plays a crucial role in digital imaging, compression, and processing. This research analyzes subjective and objective IQA methods, exploring advanced metrics such as PSNR, SSIM, MTF, and deep learning-based automated evaluation techniques. The study highlights the advantages and limitations of traditional and AI-driven assessment models, emphasizing their applicability in real-world scenarios like medical imaging, remote sensing, and video processing. Furthermore, we investigate the integration of quantum computing for IQA and its potential impact on high-dimensional image evaluation. The effectiveness of hybrid models, combining conventional and deep learning techniques, is also explored to optimize the trade-off between accuracy and computational efficiency. The findings suggest that hybrid IQA frameworks outperform single-method approaches, offering a balance between interpretability and automation. Experimental results demonstrate that AI-based IQA approaches significantly improve accuracy and reliability compared to conventional metrics. Finally, we discuss the implications of AI-based IQA models in real-time applications, including autonomous systems and augmented reality.