Limits and trade-offs of shift-invariant meta-optical encoders for image compression

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.