Combining Image Compression with Digital Image Correlation

Abstract

Digital image correlation (DIC) is a powerful experimental technique to determine displacement and strain fields. DIC methods usually require a large number of high resolution images, and this imposes significant needs on data storage and transmission. In this work, we combine digital image correlation with image compression techniques and show that it is possible to obtain accurate displacement and strain fields with only 5% of the original image size. We study two compression techniques – discrete cosine transform (DCT) and wavelet transform, and three DIC algorithms – Local Subset DIC, Global DIC and the recently proposed augmented Lagrangian DIC (ALDIC). We find that Local Subset DIC leads to the largest errors and ALDIC to the smallest when compressed images are used. We also find that wavelet-based image compression introduces less error compared to DCT image compression.

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