QuHIC: Quantization-Consistent Multi-Prior Unrolled Reconstruction for Hyperspectral Image Compression

Recent advances have demonstrated the great potential of lossy hyperspectral image (HSI) compression for airborne remote-sensing applications. However, given the limited storage capacity and transmission bandwidth of airborne sensing platforms, most existing methods insufficiently integrate compression-induced constraints with spectral–spatial priors, making it difficult to balance low bit rates and high-fidelity reconstruction. To address this challenge, we propose QuHIC, which synergizes bit-level quantized compression with multi-stage unrolled reconstruction, establishing a new paradigm for HSI compression and reconstruction. Specifically, during encoding, QuHIC employs a learnable lightweight encoder to compress an HSI into a low-dimensional quantized representation for efficient storage and transmission. During decoding, HSI reconstruction is formulated as an iterative optimization problem jointly constrained by quantization consistency and multi-prior regularization. Each iteration alternates between a quantization-consistency module (QCM) that explicitly corrects deviations from the feasible quantization interval and a multi-prior refinement network (MPRN) that serves as a learnable proximal operator integrating raw-band spectral continuity and curvature modeling, multi-scale spatial detail enhancement, and gated spectral–spatial contextual interaction. Extensive experiments demonstrate that the proposed QuHIC not only achieves efficient HSI compression at competitive bit rates but also outperforms many state-of-the-art methods in both reconstruction fidelity and spectral detail preservation.