Partial shading caused by dust accumulation, surface contaminants, and nearby obstructions reduces the incident irradiance on photovoltaic (PV) modules, leading to significant performance degradation. Conventional approaches for assessing shading effects rely on electrical measurements, irradiance sensors, or model-based calibration, increasing system complexity and limiting scalability. Although image-processing techniques have been widely investigated for PV inspection, most existing studies emphasize qualitative fault identification rather than quantitative attenuation assessment. This paper presents a non-intrusive image-processing framework that interprets PV panel images as two-dimensional illumination fields and introduces an Illumination Non-Uniformity Index (INUI) derived from physically interpretable spatial and frequency-domain descriptors. The proposed index integrates global intensity variation, spatial discontinuity, and low-frequency illumination distortion to characterize diverse shading patterns. An image-derived attenuation metric is subsequently established to quantify attenuation severity, demonstrating a monotonic relationship with illumination non-uniformity without requiring learning-based models, electrical measurements, or irradiance sensors. Experimental evaluation under diverse shading conditions confirms consistent monotonic behaviour, robustness against imaging variations, and computational efficiency suitable for large-scale visual inspection. The proposed framework provides an explainable, lightweight, and scalable methodology for quantitative image-based assessment of shading-induced attenuation in photovoltaic modules, offering a practical alternative for visual condition monitoring and supporting future intelligent PV inspection systems.