Evaluating image quality in natural scene images is challenging because scene composition is highly variable and image distortions often differ across regions. Existing image quality assessment (IQA) methods can quantify such distortions using frequency information, but are generally ineffective for sensor artifact detection and localized scoring. In this work, we propose a versatile short-time Fourier transform (STFT)-based framework for IQA, enabling intuitive spatial-frequency interpretation of localized patches. By performing spectral analysis within sliding windows, the STFT captures localized frequency characteristics that can be directly mapped back to their spatial positions. To improve interpretability, we incorporate region of interest (ROI)-aware patch extraction using Segment Anything 2 (SAM2) to focus the analysis on relevant areas. Within this framework, the same STFT representation can be flexibly adapted to multiple IQA scenarios through different spectral interpretations, including maze artifact detection, line-broken artifact detection, and texture scoring. Our experimental results demonstrate that the framework effectively identifies artifact regions and provides meaningful texture quality measurements; specifically, the proposed texture frequency metric achieves a Pearson correlation coefficient of 0.78 with subjective Elo scores. These results indicate that STFT-based spectral interpretation provides an intuitive and flexible approach for analyzing diverse image quality characteristics in natural scene images and supports practical workflows for image quality evaluation.