Reliable whole slide imaging (WSI) hinges on image quality,yet staining artefacts, defocus, and cellular degradations are common. We present DPC-QA Net, a no-reference dual-stream network that couples wavelet-based global difference perception with cellular quality assessment from nuclear and membrane embeddings via an Aggr-RWKV module. Cross-attention fusion and multi-term losses align perceptual and cellular cues. Across different datasets, our model detects staining, membrane, and nuclear issues with >92% accuracy and aligns well with usability scores; on LIVEC and KonIQ it outperforms state-of-the-art NR-IQA. A downstream study further shows strong positive correlations between predicted quality and cell recognition accuracy (e.g., nuclei PQ/Dice, membrane boundary F-score), enabling practical pre-screening of WSI regions for computational pathology.