• Ecology and Resilient Infrastructure
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• v.5 no.2
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• pp.88-93
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• 2018

In-situ water quality sampling is used for accurate water quality assessment. However, in-situ water quality sampling offers limited samples and requires much time and intensive labors. Remote sensing approach has recently applied for water quality assessment. It has shown the advantage of offering a synoptic view but also more efficient and economical. In this study, we utilized Landsat Imagery to estimate the water quality of the Tamsui River basin, considered as one of the most important rivers located in the north of Taiwan. In order to monitor water quality of Tamsui River basin, a linear regression relation between the value of spectral radiance and four water quality parameters are investigated with 38 water sampling stations. Through the regression model, we could estimate river pollution index (RPI) from the predicted value of four water quality parameters. By using RPI, we can examine the pollution level of Tamsui River. The accuracy of RPI conversion of this study ranged from 32.2% to 68.2%.

• Journal of Biomedical Engineering Research
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• v.44 no.2
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• pp.118-124
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• 2023

Many recent studies have reported that the quality of input learning data was vital to the detection of regions of interest. However, due to a lack of research on the quality of learning data on lesion detetcting using gastroscopy, we aimed to quantify the impact of quality difference in endoscopic images to lesion detection models using Image Quality Assessment (IQA) algorithms. Through IQA methods such as BRISQUE (Blind/Referenceless Image Spatial Quality Evaluation), Laplacian Score, and PSNR (Peak Signal-To-Noise) algorithm on 430 sheets of high quality data (HQD) and 430 sheets of low quality data (PQD), we showed that there were significant differences between high and low quality images in lesion detecting through BRISQUE and Laplacian scores (p<0.05). The PSNR value showed 10.62±1.76 dB on average, illustrating the lower lesion detection performance of PQD than HQD. In addition, F1-Score of HQD showed higher detection performance at 77.42±3.36% while F1-Score of PQD showed 66.82±9.07%. Through this study, we hope to contribute to future gastroscopy lesion detection assistance systems that involve IQA algorithms by emphasizing the importance of using high quality data over lower quality data.

• International Journal of Computer Science & Network Security
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• v.24 no.1
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• pp.163-177
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• 2024

The fingerprint image quality relies on the clearness of separated ridges by valleys and the uniformity of the separation. The condition of skin still dominate the overall quality of the fingerprint. However, the identification performance of such system is very sensitive to the quality of the captured fingerprint image. Fingerprint image quality analysis and enhancement are useful in improving the performance of fingerprint identification systems. A fuzzy technique is introduced in this paper for both fingerprint image quality analysis and enhancement. First, the quality analysis is performed by extracting four features from a fingerprint image which are the local clarity score (LCS), global clarity score (GCS), ridge_valley thickness ratio (RVTR), and the Global Contrast Factor (GCF). A fuzzy logic technique that uses Mamdani fuzzy rule model is designed. The fuzzy inference system is able to analyse and determinate the fingerprint image type (oily, dry or neutral) based on the extracted feature values and the fuzzy inference rules. The percentages of the test fuzzy inference system for each type is as follow: For dry fingerprint the percentage is 81.33, for oily the percentage is 54.75, and for neutral the percentage is 68.48. Secondly, a fuzzy morphology is applied to enhance the dry and oily fingerprint images. The fuzzy morphology method improves the quality of a fingerprint image, thus improving the performance of the fingerprint identification system significantly. All experimental work which was done for both quality analysis and image enhancement was done using the DB_ITS_2009 database which is a private database collected by the department of electrical engineering, institute of technology Sepuluh Nopember Surabaya, Indonesia. The performance evaluation was done using the Feature Similarity index (FSIM). Where the FSIM is an image quality assessment (IQA) metric, which uses computational models to measure the image quality consistently with subjective evaluations. The new proposed system outperformed the classical system by 900% for the dry fingerprint images and 14% for the oily fingerprint images.

• Journal of the Korean Society of Radiology
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• v.5 no.6
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• pp.421-426
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• 2011

This research was accomplished to assess dose effects on image quality at computed radiography (CR). The ultimate target of the research was finding optimized exposure that provides necessary image quality for the clinical chest diagnosis. Modulation transfer function (MTF), normalized noise power spectrum (NNPS), and Noise equivalent quanta (NEQ) corresponding to the different doses were measured for the assessment of image quality. The preparation of "edge test device" used in MTF measurement and experimental geometry setup were followed by the recommendations of International Electrotechnical Commission (IEC). The experimental results show the necessary image quality can be achieved even at a half of the automatic exposure control (AEC) setting dose for chest diagnosis. It means that the patient exposure can be reduced dramatically by using optimized dose.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2020.11a
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• pp.29-32
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• 2020

Due to the lack of improper image acquisition process, noise induction is an inevitable step. As a result, objective image quality assessment (IQA) plays an important role in estimating the visual quality of noisy image. Plenty of IQA methods have been proposed including traditional signal processing based methods as well as current deep learning based methods where the later one shows promising performance due to their complex representation ability. The deep learning based methods consists of several convolution layers and down sampling layers for feature extraction and fully connected layers for regression. Usually, the down sampling is performed by using max-pooling layer after each convolutional block. We reveal that this max-pooling causes information loss despite of knowing their importance. Consequently, we propose a better IQA method that replaces the max-pooling layers with strided convolutions to down sample the feature space and since the strided convolution layers have learnable parameters, they preserve optimal features and discard redundant information, thereby improve the prediction accuracy. The experimental results verify the effectiveness of the proposed method.

• Journal of Environmental Impact Assessment
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• v.15 no.2
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• pp.121-127
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• 2006

The conventional water quality measurements by point sampling provide only site specific temporal water quality information but not the synoptic geographic coverage of water quality distribution. To circumvent these limitations in temporal and spatial measurements, the use of remote sensing is increasingly involved in the water quality monitoring research. In other to assess a trophic state of Yongdam reservoir using satellite imagery data, I obtained Landsat ETM data and water quality data on 16th September and 18th October 2001. The approach involved acquisition of water quality samples from boats at 33 sites on 16th September and 30 sites on 18th October 2001, simultaneous with Landsat-7 satellite overpass. The correlation coefficients between the DN values of the imagery and the concentrations of chlorophyll-a were analyzed. The visible bands(band 1,2,3) and near infrared band(band 4) data of September image showed the correlation coefficient values higher than 0.9. The October image showed the correlation coefficient values about 0.7 due to the atmospheric effect and low variation of chlorophyll-a concentration. Regression models between the chrophyll-a concentration and DN values of the Landsat imagery data have been developed for each image. The regression model was determined based on the spectral characteristics of chlorophyll, so the green band(band 2) and near infrared band(band 4) were selected to generate a trophic state map. The coefficient of determination(R2) of the regression model for 16th September was 0.95 and that of the regression model for 18th October was 0.55. According to the trophic state map made based on Aizaki's TSI and chlorophyll-a concentration, the trophic state of Yongdam reservoir was mostly eutrophic state during this study.

• Journal of Korean Tunnelling and Underground Space Association
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• v.26 no.4
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• pp.365-384
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• 2024

The development of scanning technology is accelerating for safer and more efficient automated inspection than human-based inspection. Research on automatically detecting facility damage from images collected using computer vision technology is also increasing. The pixel size, quality, and quantity of an image can affect the performance of deep learning or image processing for automatic damage detection. This study is a basic to acquire high-quality raw image data and camera performance of a mobile tunnel scanning system for automatic detection of damage based on deep learning, and proposes a method to quantitatively evaluate image quality. A test chart was attached to a panel device capable of simulating a moving speed of 40 km/h, and an indoor test was performed using the international standard ISO 12233 method. Existing image quality evaluation methods were applied to evaluate the quality of images obtained in indoor experiments. It was determined that the shutter speed of the camera is closely related to the motion blur that occurs in the image. Modulation transfer function (MTF), one of the image quality evaluation method, can objectively evaluate image quality and was judged to be consistent with visual observation.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.45 no.3
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• pp.64-73
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• 2008

In this paper, we propose a novel objective video quality assessment method from encoded H.264/AVC.. Conventional algorithms have been proposed to assess video/image quality with image frames reconstructed in a decoder side. On the other hand, the proposed assessment is conducted with the syntax elements which are embedded in a bitstream. The proposed BS-based algorithm makes use of the statistics of boundary strength(BS) which are employed in the H.264/AVC. The proposed algorithm has lower computational complexity than conventional methods, EPSNR and Blockiness, resulting that it can accomplish assessment of the video quality in real time. Furthermore, the accuracy of the proposed video quality assessment is about 32% and 65% better than several conventional algorithms.

• Korean Journal of Remote Sensing
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• v.30 no.1
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• pp.1-12
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• 2014

The purpose of this paper is to provide a compact overview of the state-of-art image mosaic algorithms in commercial softwares and to propose objective assessment method of that. Among them, several algorithms, widely used and high quality, result in the mosaic image by applying to seven different kinds of seasons of KOMPSAT-2 images and then consequently each result is analyzed visually. Moreover, quality index is suggested to assess the similarity with colors regarding adjacency images and then it is performed by comparing and analyzing the visual and quantitative results. Consequently, we found out the suggested quality index is feasible.

• Science of Emotion and Sensibility
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• v.16 no.3
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• pp.373-386
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• 2013

In this research, we specify the image consumers' preferred image quality ranges based on objective image quality evaluation factors and follow a method which measures preference of the natural image scenes. In other words, according to No-Reference, we select dynamic range, color, and contrast as factors of image quality measurements. For collecting sample images, we choose the preferred 200 landscapes which have over 30 recommendations by image consumers on the internet photo gallery. According to the scores of three objective factors of image quality measurements, the final expected score which means the image quality preference is measured and its total score is 100 points. In the main test, the actual image sample shows dynamic range 10 stop, LAB mean value L:54.7, A:2.96, B:-15.84, and RSC contrast 376.9. Total 200 image samples' normal distribution z value represents in dynamic range 0.21, LAB mean value L:0.15, A:0.38, B:0.13, and RSC contrast 0.08. In the standard normal distribution table, we can convert the z value as a percentage; dynamic range is 8.32%, LAB mean value is L:5.96%, A:14.8%, B:5.17%, and RSC contrast is 3.19%. And then, we convert the percentage values into the scores of 100; dynamic range is 91.68, LAB mean value is 91.36, and RSC contrast is 96.81. Therefore, we can conclude that the sample image's total mean score is 94.99 based on three objective image quality factors. Throughout our proposed image quality assessment model, we can measure the preference value of natural scenes. Also, we can specify the preferred image quality representation ranges and measure the expected image quality preference.