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The existing literatures in SHM that implement image compression methods are mainly for structural health diagnosis or for training purposes using advanced convolutional neural networks (CNNs) or deep learning frameworks. Comprehensive reviews of image and video compression with neural network can be found in Dony et al. [65], Jiang et al. [66] and Ma et al. [67]. Studies conducted by Yang and Nagarajaiah [68] and Huang et al. [69], reconstructed SHM images based on the CS theory that crack information in a structure is more pronounced and exhibits sparsity in the image. Xu et al. [70] resized the resolution of rectangular raw images of damaged reinforced concrete columns into smaller square pixels image to reduce calculation costs on the supervised learning procedure. Su [71] downsized 100 sampling images that were used in training model for concrete pavement to reduce computational time. Xu et al. [72] resized the height pixel unit of grayscale images using bicubic interpolation then cropped them into smaller elements as the input of the deep network. These data were used to train the machine algorithms for identification framework of steel surface cracks.

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To monitor the building vibration and seismic performance, SHM was conducted using two-types of vision-based sensor systems. The first system used the same high-speed cameras as in the one-inch block and quasi-static tests, which is shown as Cam 1 and Cam 2 in Figure 14. The second system used consumer-grade digital cameras that was set to monochrome mode, which is shown as Cam A and Cam B in Figure 14. No additional lights were used in the monitoring, so both vision systems relied completely on the ambient light sources and the setting adjustment in each camera. Thus, it is noted that the captured photogrammetry images as well as the test images required image processing to enhance their low-level of brightness and contrast before resampling. Since image enhancement is not the scope of this study, the process is not provided here, and only discussion on the image compression effects on dynamic measurements of vision systems is provided here. Table 8 provides more details on the two vision sensor systems and their configuration. Both systems used CMOS sensor. However, the .jpg format was used as opposed to the .tiff for the digital cameras versus the high-speed cameras and the input image resolution was unchanged. A lower resolution was set in the digital camera even though it could shoot image at the maximum of 5184 3456 pixels (18 MP). The reason was that the video recording mode was used for the seismic test monitoring instead of continuous burst image recording. Therefore, image resolution of 1920 1080 pixels was set for photogrammetry process to be similar with the 1920 1080 full HD video. The sampling rate for the seismic test was selected as 32 fps for the high-speed cameras, which resulted in 7680 images for 120 sec recording duration and the total data storage of 38.4 GB in both cameras. The digital camera monitored the tests through video recording with the HD quality of 1920 1080 pixels. After video processing, the total image data storage for the system was computed as 2.3 GB.

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