Anamorphic transformation and its application to time-bandwidth compression

TL;DR

This paper introduces a novel anamorphic transformation technique for analog signals that enables lossless time-bandwidth compression, feature-selective stretching, and digital data reduction, inspired by biological and artistic principles.

Contribution

It presents a general, physics-based method for analog signal compression and feature enhancement that is also applicable as a digital data compression algorithm.

Findings

Achieved lossless time-bandwidth compression in experiments.

Enabled feature-selective signal stretching for better temporal resolution.

Reduced digital data size while preserving signal integrity.

Abstract

A general method for compressing the modulation time-bandwidth product of analog signals is introduced and experimentally demonstrated. As one of its applications, this physics-based signal grooming performs feature-selective stretch, enabling a conventional digitizer to capture fast temporal features that were beyond its bandwidth. At the same time, the total digital data size is reduced. The compression is lossless and is achieved through a same-domain transformation of the signal's complex field, performed in the analog domain prior to digitization. Our method is inspired by operation of Fovea centralis in the human eye and by anamorphic transformation in visual arts. The proposed transform can also be performed in the digital domain as a digital data compression algorithm to alleviate the storage and transmission bottlenecks associated with "big data".