In high-definition imaging, solving the problem of out-of-focus blurring is one of the core challenges. For example, focus drift in telephoto lenses for distant targets affects subsequent analysis, necessitating a quantitative assessment of blur. Existing methods have drawbacks: subjective assessment lacks accuracy; reference-based objective methods rely on original, clear images, often unavailable in practice; feature-based no-reference methods have limitations and may misjudge complex images. For instance, EMBM is less sensitive to weak edges. Thus, a time-frequency domain no-reference assessment algorithm is proposed, with core innovations: first, a multi-scale feature extraction model integrating time-domain and frequency-domain features to comprehensively capture edge information across dimensions; second, a principal component analysis feature optimization module for dimensionality reduction and redundancy removal, enhancing key feature representation; finally, a dynamic weight allocation mechanism that specifically increases weak edge feature weights, solving EMBM weak edge neglect. Tests on the TID2013 dataset show that its SROCC index is \(1.39\%\) and \(2.44\%\) higher than that of EMBM, respectively.