Abstract
This research focuses on the verification of the viability of image compression in infrared thermography in order to address the problem of data storage. Specifically, images from vibrothermographic tests were utilized due to their special characteristics compared to the results from alternative infrared thermography techniques, which are able to introduce additional uncertainties to the compression process. In this research, an adaptive algorithm based on the lifting discrete wavelet transform and set-partitioning embedded blocks was used for image compression. Five different methods, namely the compression ratio, mean squared error, peak signal-to-noise ratio, structural similarity index and coordinate modal assurance criterion, were applied to evaluate the performance of the compression process while identifying and locating the regions affected more significantly after image compression. Feature extraction through the independent component analysis was then applied to the images to separate the features such as the hot spots so that the influence from the image compression process on each important feature could be evaluated independently. In this article, the theoretical background of the applied data processing techniques is firstly presented. Through two sets of data acquired from vibrothermographic tests on an aerospace-grade composite plate containing delamination, the effects of the image compression process on the relevant hot spots are evaluated, and the effectiveness of the compression process is verified. It is demonstrated that the compression process was able to reduce the size of the images significantly without adversely affecting the quality of the important features indicating the presence of damage. The major characteristics of the key features have been successfully preserved after effective image compression.
Highlights
In the area of non-destructive testing and evaluation (NDT&E), infrared thermography (IRT) has evolved rapidly during the past decades thanks to the advances in infrared (IR) cameras and lenses
The extracted components in this research were further processed so that the hot spots always appear as red in the generated images of the extracted components. Another consequence of this limitation is that the values of the elements in the components extracted by FastICA are not equivalent to the original temperature data due to the change in scale, the scale does not affect the relative magnitude of data across pixels in each extracted component
Research outputs focusing on the verification of the viability of applying image compression to results from vibrothermographic tests is presented
Summary
In the area of non-destructive testing and evaluation (NDT&E), infrared thermography (IRT) has evolved rapidly during the past decades thanks to the advances in infrared (IR) cameras and lenses. Due to the uncomplicated demonstration of measurement results, these images are relatively easy to analyze These advantages make IRT attractive in structural health monitoring and damage detection, especially when applied to large engineering structures where the instrumentation and measurement time required for other non-contact methods is usually considerably longer while the contact sensing— such as accelerometers and strain gauges—would miss large areas of the structure. These easy-to-process high-definition images and videos come at a price. The original images and the compressed data, as well as their respective extracted features, were compared to enable the consideration of the influence of the image compression process on the important features in the thermal images, such as the hot spots indicating the presence of damage, so that the viability of image compression in IRT, vibrothermography, could be demonstrated
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