Abstract
Deep learning-based image compression achieves remarkable average rate-distortion performance but is prone to failure on noisy, high-frequency, or high-entropy inputs. This work systematically investigates these failure cases and proposes a noise-aware hybrid compression framework to address them. A High-Frequency Vulnerability Index (HFVI) is proposed, integrating frequency energy, encoder Jacobian sensitivity, and texture entropy into a unified measure of degradation susceptibility. Guided by HFVI, the system incorporates a selective zero-shot denoising module (P2PA) and a lightweight hybrid codec selector that determines, for each image, whether P2PA is necessary and selecting the more reliable codec (a learning-based model or JPEG2000) accordingly, without retraining any compression backbones. Experiments span a 200,000-image cross-domain benchmark incorporating general datasets, synthetic noise (eight levels), and real-noise datasets demonstrate that the proposed pipeline improves PSNR by up to 1.28 dB, raises SSIM by 0.02, reduces LPIPS by roughly 0.05, and decreases the failure-case rate by 6.7% over the best baseline (Joint-IC). Additional intensity-profile and cross-validation analyses further validate the robustness and deployment readiness of the method, showing that the hybrid selector provides a practical path toward reliable, noise-adaptive deep image compression.