Convolutional Optical Encoders for Generalizable Image Compression

TL;DR

This paper explores the use of meta-optical encoders with shift-invariant PSFs for efficient, parallel image compression, comparing different encoding strategies and their robustness and quality at similar compression ratios.

Contribution

It systematically evaluates various PSF encoding strategies combined with digital reconstruction, highlighting the trade-offs in quality and noise robustness.

Findings

Spatial binning achieves highest reconstruction quality at same compression ratio.

Multi-channel methods are more robust to noise.

Optical encoders enable early data reduction in resource-constrained systems.

Abstract

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.