Comparative Compression Robustness Evaluation of Digital Image Forensics

Remy, Oliver; Strumegger, Sebastian; Hämmerle-Uhl, Jutta; Uhl, Andreas

doi:10.1007/978-3-031-10450-3_19

Cited by 2 publications

(1 citation statement)

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“…The impact of (JPEG) compression on general CBIR accuracy has been investigated [47][48][49], while specific (medical) image retrieval-focusing on high-magnification colonoscopy was addressed [46] considering more variety in the compression schemes. Similar investigations have been conducted in remote sensing [50][51][52] as well as in image forensics, such as for image age determination [53], copy-move detection, and PRNU-based device identification [54]. In microscopy imaging, being close to the particular scenario we address in this paper, ref.…”

Section: Other Areasmentioning

confidence: 59%

Impact of Image Compression on In Vitro Cell Migration Analysis

2023

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Collective cell movement is an indication of phenomena such as wound healing, embryonic morphogenesis, cancer invasion, and metastasis. Wound healing is a complicated cellular and biochemical procedure in which skin cells migrate from the wound boundaries into the wound area to reconstruct the injured skin layer(s). In vitro analysis of cell migration is an effective assay for measuring changes in cell migratory complement in response to experimental inspections. Open-source segmentation software (e.g., an ImageJ plug-in) is available to analyze images of in vitro scratch wound healing assays; however, often, these tools are error-prone when applied to, e.g., low-contrast, out-of-focus, and noisy images, and require manual tuning of various parameters, which is imprecise, tedious, and time-consuming. We propose two algorithmic methods (namely log gradient segmentation and entropy filter segmentation) for cell segmentation and the subsequent measurement of the collective cell migration in the corresponding microscopic imagery. We further investigate the effects of image compression on the algorithms’ measurement accuracy, applying lossy compression algorithms (the current ISO standards JPEG2000, JPEG, JPEG-XL and AV1, BPG, and WEBP). We aim to identify the most suitable compression algorithm that can be used for this purpose, relating rate–distortion performance as measured in terms of peak signal-to-noise ratio (PSNR) and the multiscale structural similarity index (MS-SSIM) to the segmentation accuracy obtained by the segmentation algorithms. The experimental results show that the log gradient segmentationalgorithm provides robust performance for segmenting the wound area, whereas the entropy filter segmentation algorithm is unstable for this purpose under certain circumstances. Additionally, the best-suited compression strategy is observed to be dependent on (i) the segmentation algorithm used and (ii) the actual data sequence being processed.

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