Abstract: In the Blind Image Quality Assessment (BIQA) field, accurately assessing the quality of authentically distorted images presents a substantial challenge due to the diverse distortion types in natural settings. Existing State-of-the-art IQA methods mix a sequence of distortions to entire images to establish distortion priors, but are inadequate for images with spatially local distortions. To address this, we introduce a novel IQA framework that employs knowledge distillation tailored to perceive spatially heterogeneous distortions, enhancing quality-distortion awareness. Specifically, we introduce a novel Block-wise Degradation Modelling approach that applies distinct distortions to different spatial blocks of an image, thereby expanding varied distortion priors. Following this, we present a Block-wise Aggregation and Filtering module that enables fine-grained attention to the quality perception within different distortion areas of the image. Furthermore, to enhance the granularity of distortion perception across various regions while maintaining quality perception, we incorporate strategies of Contrastive Knowledge Distillation and Affinity Knowledge Distillation to learn the distortion discrimination power and distortion correlation of different regions, respectively. Extensive experiments on seven standard BIQA datasets demonstrate the superior performance to the state-of-the-art BIQA methods, i.e., achieving the PLCC values of 0.947 ($\uparrow 1.2\%$ vs. 0.936 in KADID) and 0.735 ($\uparrow 8.3\%$ vs. 0.679 in LIVEFB).