.png){kind=icon}
.png){kind=icon}
Return
Improving perceptual quality in low-bitrate remote sensing image compression via codebook priors and adversarial learning
.png){kind=icon}
PRE
AI
J
X
DOI：10.1016/j.displa.2026.103445.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
Existing image compression algorithms have achieved significant advancements in preserving high fidelity. However, attaining superior perceptual quality often requires retraining a compression model specifically optimized for this objective, which incurs additional training costs and hampers rapid deployment. To address this limitation, we propose CodeIC, an innovative framework that leverages codebook priors and adversarial learning to enhance perceptual quality without the need for retraining existing high-fidelity compression models. CodeIC operates through three meticulously designed stages. First, we pretrain a high-quality discrete codebook using a redesigned vector quantized generative adversarial network (VQGAN), which serves as a robust generative prior. Second, we introduce a Transformer-based prediction model to align the decoded image features from an existing compression algorithm with the frozen high-quality codebook. Finally, we design a hierarchical prior integration network (HPIN), which integrates Swin Transformer blocks (STBs) and multi-head cross-attention modules (MCMs) to query hierarchical priors from the codebook, enhanced by adversarial learning. This approach significantly improves the decoding of texture-rich images, ensuring both perceptual quality and fidelity. Extensive experiments demonstrate that CodeIC achieves a favorable trade-off between perception and fidelity, delivering superior perceptual quality than high-fidelity compressors and higher fidelity than perception-oriented models. More importantly, it substantially boosts downstream task performance (e.g., salient object detection) over baseline methods, proving its preservation of critical semantic information.
Keywords:
Remote sensing image compression
Codebook
Adversarial learning
Multi-head cross-attention mechanism
Journal
IF:
3.4
Papers:
2.1K
Citations:
3.2K
Organization
Cited Papers