# MetaRanker: Human-in-the-loop Active Ranking for Metalens Image Quality Source: [MetaRanker: Human-in-the-loop Active Ranking for Metalens Image Quality]() Collection index: [llms.txt]() Data updated: 2026-07-01T05:00:18.486Z Authors: Yujin Park, Haejun Chung, Ikbeom Jang Published: 2026-05-28T00:51:48.000Z ## Abstract Image quality in modern imaging systems emerges from the coupled effects of the sensor, optics, and computational reconstruction. Ultra-thin metalenses offer a path toward substantial miniaturization of optical modules, but practical designs often exhibit pronounced chromatic and field-dependent aberrations that necessitate computational reconstruction. In current metalens pipelines, reconstruction models are commonly trained and selected using distortion-based fidelity objectives, such as PSNR, yet these proxies can be weakly correlated with human preference and downstream utility, reflecting the well-known perception--distortion trade-off. We introduce MetaRanker, a human-in-the-loop active ranking framework that formalizes metalens image quality in terms of semantic interpretability, defined as the degree to which humans can reliably recognize objects and structures in the presence of optical artifacts. MetaRanker combines a probabilistic preference model with uncertainty-aware query selection, and leverages vision--language models to provide lightweight semantic priors. Importantly, these priors are used only to guide the sampling of informative comparisons; human judgments remain the primary supervision signal throughout. Across real-world and synthetic metalens datasets with distinct degradation profiles, MetaRanker produces rankings that align most closely with human assessments, while reducing the number of pairwise annotations required by approximately 80% relative to exhaustive pairwise evaluation. Finally, we show that standard image quality assessment metrics exhibit limited alignment with human interpretability in the metalens domain, positioning MetaRanker as a practical step toward perceptually grounded metalens evaluation and co-design. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. - [Metalens image quality grading on DRMI Animal](): Metalens image quality grading; DRMI Animal; Kendall Correlation: 0.695. - [Metalens image quality grading on Metaformer Animal](): Metalens image quality grading; Metaformer Animal; Kendall Correlation: 0.794. - [Metalens image quality grading on DRMI Landscape](): Metalens image quality grading; DRMI Landscape; Kendall's Tau: 0.786. - [Metalens image quality grading on Metaformer Landscape](): Metalens image quality grading; Metaformer Landscape; Kendall Correlation: 0.84. ## Original sources and implementation [Original paper]() [PDF]()