<p>Pure chromatic background images (PCBIs) consist of large uniform regions with minimal structural content, which makes standard perceptual hashing based on low-frequency DCT unreliable. High similarity scores may be assigned to visually different images, leading to false acceptance. In blockchain-based copyright registration systems, such transactions are irreversible once confirmed, necessitating a conservative similarity model. This paper proposes DBpHash, a dual-band perceptual hashing framework for blockchain-based copyright registration and verification of PCBIs. During computation, low-frequency DCT bands are excluded while mid and high-frequency bands are energy-normalized and binarized to create a 128-bit hash. Similarity is calculated independently for each band and fused using inverse-variance weighting. Thresholds are derived from real and imposter distributions. Experiments conducted on a PCBI dataset are further validated via cross-dataset evaluation on BSDS500 and DTD without retraining. False positive rate remains at 0.095 while similarity exceeds 0.92 under common photometric distortions. Statistical analysis confirms strong hash properties, including near-ideal bit balance and entropy. The framework is integrated with blockchain-based registration and off-chain similarity computation. Results indicate that DBpHash provides a reliable and distortion-resilient solution for copyright authentication of PCBIs.</p>