Abstract
Full-Reference image quality assessment (FR IQA) is important for image compression, restoration and generative modeling, yet current neural metrics remain slow and vulnerable to adversarial perturbations. We present BiRQA, a compact FR IQA metric model that processes four fast complementary features within a bidirectional multiscale pyramid. A bottom-up attention module injects fine-scale cues into coarse levels through an uncertainty-aware gate, while a top-down cross-gating block routes semantic context back to high resolution. To enhance robustness, we introduce Anchored Adversarial Training, a theoretically grounded strategy that uses clean "anchor" samples and a ranking loss to bound pointwise prediction error under attacks. On five public FR IQA benchmarks BiRQA outperforms or matches the previous state of the art (SOTA) while running ~3x faster than previous SOTA models. Under unseen white-box attacks it lifts SROCC from 0.30-0.57 to 0.60-0.84 on KADID-10k, demonstrating substantial robustness gains. To our knowledge, BiRQA is the only FR IQA model combining competitive accuracy with real-time throughput and strong adversarial resilience.
Abstract (translated)
全参考图像质量评估（FR IQA）在图像压缩、恢复和生成模型中非常重要，然而目前的神经网络度量方法仍然速度慢且易受对抗性干扰。我们提出了一种名为BiRQA的小巧的全参考图像质量评估模型，它在一个双向多尺度金字塔内处理四种快速互补特征。通过一个不确定性感知门，自下而上的注意力模块将细粒度线索注入到粗粒度级别，而自上而下的交叉门控块则将语义上下文传递回高分辨率区域。为了增强其鲁棒性，我们引入了基于理论依据的锚定对抗训练策略，该策略利用干净的“基准”样本和排名损失来限制在攻击情况下逐点预测误差。在五个公开的全参考图像质量评估基准测试中，BiRQA的表现超越或匹配之前的最先进模型（SOTA），同时运行速度比之前的SOTA模型快约3倍。在未见过的白盒攻击下，BiRQA将KADID-10k数据集上的SROCC值从0.30至0.57提高到了0.60至0.84之间，展示了显著的对抗性鲁棒性提升。据我们所知，BiRQA是第一个结合了高精度、实时吞吐量和强大抗干扰能力的全参考图像质量评估模型。
URL
https://arxiv.org/abs/2602.20351
