Toward Trustworthy On-Device AI: A Quantization-Robust Parameterized Hybrid Neural Filtering Framework

Recent advances in deep learning have led to a proliferation of AI services for the general public. Consequently, constructing trustworthy AI systems that operate on personal devices has become a crucial challenge. While on-device processing is critical for privacy-preserving and latency-sensitive applications, conventional deep learning approaches often suffer from instability under quantization and high computational costs. Toward a trustworthy and efficient on-device solution for image processing, we present a hybrid neural filtering framework that combines the representational power of lightweight neural networks with the stability of classical filters. In our framework, the neural network predicts a low-dimensional parameter map that guides the filter’s behavior, effectively decoupling parameter estimation from the final image synthesis. This design enables a truly trustworthy AI system by operating entirely on-device, which eliminates the reliance on servers and significantly reduces computational cost. To ensure quantization robustness, we introduce a basis-decomposed parameterization, a design mathematically proven to bound reconstruction errors. Our network predicts a set of basis maps that are combined via fixed coefficients to form the final guidance. This architecture is intrinsically robust to quantization and supports runtime-adaptive precision without retraining. Experiments on depth map super-resolution validate our approach. Our framework demonstrates exceptional quantization robustness, exhibiting no performance degradation under 8-bit quantization, whereas a baseline suffers a significant 1.56 dB drop. Furthermore, our model’s significantly lower Mean Squared Error highlights its superior stability, providing a practical and mathematically grounded pathway toward trustworthy on-device AI.