Convolutional Optical Encoders for Generalizable Image Compression

We investigate the utility of meta-optical encoders for generalizable image compression by leveraging their intrinsic shift-invariant point spread functions (PSFs). Compared with purely digital approaches, such optical encoders offer parallel and energy-efficient compression, enabling early data reduction prior to electronic processing and transmission, which is particularly attractive for resource-constrained and compact imaging systems. Although the operations realizable by a single passive optical layer remain fundamentally constrained, we systematically study several PSF encoding strategies combined with a total-variation (TV) digital reconstruction backend. Specifically, under identical compression ratios, we compare spatial binning, multi-channel random, and multi-channel orthogonal PSF based designs. Our results show that, at the same compression ratios, spatial binning achieves the highest reconstruction quality among all encoding strategies; however, it exhibits limited robustness to noise compared with multi-channel methods.