BiRQA: Bidirectional Robust Quality Assessment for Images

• URL: http://arxiv.org/abs/2602.20351v1
• Date: Mon, 23 Feb 2026 20:52:56 GMT
• Title: BiRQA: Bidirectional Robust Quality Assessment for Images
• Authors: Aleksandr Gushchin, Dmitriy S. Vatolin, Anastasia Antsiferova,
• Abstract summary: Full-Reference image quality assessment (FR IQA) is important for image compression, restoration and generative modeling.<n>We present BiRQA, a compact FR IQA metric model that processes four fast complementary features within a bidirectional multiscale pyramid.<n>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.
• Score: 49.74447451098852
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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