Adaptive Run-Length Encoded DCT: A High-Fidelity Compression Algorithm for Real-Time Photoacoustic Microscopy Imaging in LabVIEW.

Abstract

Continuous photoacoustic microscopy (PAM) imaging generates large volumes of data, resulting in significant storage demands. Here, we propose a high-fidelity real-time compression algorithm for PAM data in LabVIEW by combining Discrete Cosine Transform (DCT) with adaptive thresholding and Run Length Encoding (RLE), which we term Adaptive Run Length Encoded DCT (AR-DCT) compression. This algorithm reduces data storage requirements while preserving all the details of the images. AR-DCT ensures real-time compression, achieving superior compression ratios (CRs) compared to traditional DCT compression. We evaluated the performance of AR-DCT using in vivo mouse brain imaging data, demonstrating a CR of ~50, with a structural similarity index of 0.980 and minimal degradation in signal quality (percentage-root-mean-square-difference of 1.345%). The results show that AR-DCT outperforms traditional DCT, offering higher compression efficiency without significantly sacrificing image quality. These findings suggest that AR-DCT provides an effective solution for applications requiring continuous experiments, such as cerebral hemodynamics studies.