Effects of JPEG XR Compression Settings on Iris Recognition Systems

Horvath, Kurt; Stögner, Herbert; Uhl, Andreas

doi:10.1007/978-3-642-23678-5_7

Cited by 24 publications

(4 citation statements)

References 9 publications

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“…A further example is the optimization of JPEG 2000 Part 2 wavelet packet decomposition structures with respect to optimizing iris [36] and fingerprint [37] recognition accuracy, which provided better results than rate-distortion optimized wavelet packet structures. Even JPEG XR was optimized for the iris recognition context [38].…”

Section: Biometricsmentioning

confidence: 99%

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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Section: Biometricsmentioning

confidence: 99%

Impact of Image Compression on In Vitro Cell Migration Analysis

2023

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“…Konrad et al [16] investigate tuning opportunities for JPEG and show, that by customizing quantization tables in JPEG, recognition performance can be increased. In [13] effects of JPEG-XR compression on iris recognition are examined. JPEG-XR is found to be competitive to the current standard JPEG 2000 while exhibiting significantly lower computational demands.…”

Section: Related Workmentioning

confidence: 99%

Effects of severe image compression on iris segmentation performance

Rathgeb

Uhl

Wild

2014

IEEE International Joint Conference on Biometrics

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The International Organization for Standardization (ISO) specifies iris biometric data to be recorded and stored in (raw) image form (ISO/IEC 19794-6), rather than in extracted templates, i.e. iris-codes. Existing literature confirms the applicability of lossy image compression in iris biometric systems, however, so far investigations on the impact of image compression on iris segmentation algorithms have remained elusive.In this work we examine the impact of severe image compression algorithms in particular, JPEG, JPEG 2000, and JPEG-XR, on the performance of different iris segmentation approaches. Experiments are carried out on an uncompressed iris database and, based on a manually annotated ground truth, effects of image compression on iris segmentation are quantified. It is found that surprisingly, JPEG causes the least segmentation errors over a wide range of high to medium bitrates (down to 0.3 bpp) despite of its weak performance in terms of PSNR rate-distortion behaviour.

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“…Due to its lower complexity compared to JPEG2000 we wanted to evaluate if there is also a difference in terms of face detection. In the work of Horvat, Stögner and Uhl, JPEG XR was analyzed on Iris Recognition Systems [7] and they concluded that JPEG XR performed better than JPEG2000.…”

Section: Jpeg Xrmentioning

confidence: 99%

Exploring Compression Impact on Face Detection Using Haar-like Features

et al. 2015

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The main goal in our experimental study was to explore the impact of image compression on face detection using Haar-like features. In our setup we used the JPEG, JPEG2000 and JPEG XR compression standards to compress images from selected databases at given compression ratios. We performed the face detection using OpenCV on the reference images from the database as well as on the compressed images. After the detection process we compared the detected areas between the reference and the compressed image gaining the average coverage, false positive and false negative areas. Experimental results comparing JPEG, JPEG2000 and JPEG XR are showing that the average coverage of the detected face area differ between 79,58% in the worst and 99,61% in the best case. The false negative (not covered) areas range between 0,33% and 19,75% and false positive (fallout) areas between 0,38% and 9,45%. We conclude that the JPEG compression standard is performing worse than JPEG2000 and JPEG XR while both latter providing quite equal and good results.

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Effects of JPEG XR Compression Settings on Iris Recognition Systems

Abstract: Abstract. JPEG XR is considered as a lossy sample data compression scheme in the context of iris recognition techniques. It is shown that apart from low-bitrate scenarios, JPEG XR is competitive to the current standard JPEG2000 while exhibiting significantly lower computational demands.

Cited by 24 publications

References 9 publications

Impact of Image Compression on In Vitro Cell Migration Analysis

Impact of Image Compression on In Vitro Cell Migration Analysis

Effects of severe image compression on iris segmentation performance

Exploring Compression Impact on Face Detection Using Haar-like Features

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