This academic survey comprehensively examines Image Quality Assessment (IQA), a critical area in image processing and computer vision. It categorizes and discusses both general and specialized IQA methods, ranging from traditional statistical and machine learning approaches to cutting-edge deep learning models like CNNs and Transformers. The document highlights the advantages and limitations of current techniques, emphasizing the necessity for distortion-specific and application-tailored IQA solutions. Ultimately, the authors advocate for future IQA development to prioritize practicality, interpretability, and ease of implementation within specific application contexts.

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