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Lett."],"published-print":{"date-parts":[[2026,10,1]]},"abstract":"<jats:p>Meta-optical encoders can reduce image data before electronic readout or transmission, but engineered point-spread functions (PSFs) do not automatically outperform conventional imaging. We study scene-agnostic, shift-invariant, linear optical encoders using both a fixed total-variation (TV) reconstruction backend and a learned YOLOv8 detection backend. Under a measurement-budget definition of compression ratio that counts all sensed samples across all channels, we compare lens imaging with spatial binning, positive random multi-channel PSFs, signed random kernels, and orthogonal multi-channel kernels. In the low-noise regime, lens-binning gives the highest reconstruction fidelity and strongest YOLOv8 detection metrics at the same compression ratio. Multi-channel encoders, however, degrade more slowly under measurement noise because the measurements are distributed across complementary channels. These results show that, for scene-agnostic incoherent imaging, engineered convolutional PSFs should be justified primarily by robustness, multiplexing, or downstream system constraints, rather than by an expectation that generic wavefront coding will outperform lens-based binning.<\/jats:p>","DOI":"10.1364\/ol.608423","type":"journal-article","created":{"date-parts":[[2026,9,3]],"date-time":"2026-09-03T12:01:21Z","timestamp":1788436881000},"page":"5518","update-policy":"https:\/\/doi.org\/10.1364\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Limits and trade-offs of shift-invariant meta-optical encoders for image compression"],"prefix":"10.1364","volume":"51","author":[{"ORCID":"https:\/\/orcid.org\/0009-0000-3680-1203","authenticated-orcid":true,"given":"Yubo","family":"Zhang","sequence":"first","affiliation":[{"id":[{"id":"https:\/\/ror.org\/00cvxb145","id-type":"ROR","asserted-by":"publisher"}]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8492-729X","authenticated-orcid":true,"given":"Rui","family":"Chen","sequence":"additional","affiliation":[{"id":[{"id":"https:\/\/ror.org\/00cvxb145","id-type":"ROR","asserted-by":"publisher"}]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhihao","family":"Zhou","sequence":"additional","affiliation":[{"id":[{"id":"https:\/\/ror.org\/00cvxb145","id-type":"ROR","asserted-by":"publisher"}]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0917-590X","authenticated-orcid":true,"given":"Arka","family":"Majumdar","sequence":"additional","affiliation":[{"id":[{"id":"https:\/\/ror.org\/00cvxb145","id-type":"ROR","asserted-by":"publisher"}]},{"name":"University of Washington"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"285","published-online":{"date-parts":[[2026,9,28]]},"reference":[{"key":"ol-51-19-5518-R1","doi-asserted-by":"publisher","first-page":"14807","DOI":"10.1364\/OE.19.014807","type":"journal-article","volume":"19","author":"Shechtman","year":"2011","journal-title":"Opt. 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All rights, including for text and data mining (TDM), Artificial Intelligence (AI) training, and similar technologies, are reserved.","name":"copyright","label":"Copyright"}]}}