• Journal of KIISE
• /
• v.41 no.10
• /
• pp.847-858
• /
• 2014

Until now, a lot of image quality assessment techniques or tools for optimal human visual system(HVS)-awareness have been researched and SSIM(Structural SIMilarity) and its improved techniques are representative examples. However, they often cannot cope with various images and different distortion types robustly, and thus this can cause a large gap between their index values and HVS-awareness. In this paper, we conduct image quality assessment on SSIM and its variants intensively and analyze the causes of each component function's observed anomalies. Then, we propose a novel image quality assessment technique to compensate and improve such anomalies. Additionally, through extensive image assessment simulations, we show that the proposed technique can indicate HVS-awareness more robustly and consistently than SSIM and its variants for various images and different distortion types.

• Journal of KIISE
• /
• v.42 no.10
• /
• pp.1294-1302
• /
• 2015

A lot of image quality assessment metrics that can precisely reflect the human visual system (HVS) have previously been researched. The Structural SIMilarity (SSIM) index is a remarkable HVS-aware metric that utilizes structural information, since the HVS is sensitive to the overall structure of an image. However, SSIM fails to deal with color difference in terms of the HVS. In order to solve this problem, the Structural and Hue SIMilarity (SHSIM) index has been selected with the Hue, Saturation, Intensity (HSI) model as a color space, but it cannot reflect the HVS-aware color difference between two color images. In this paper, we propose a new image quality assessment method for a color image by using a CIE Lab color space. In addition, by using a support vector machine (SVM) classifier, we also propose an optimization system for applying optimal metric according to the types of distortion. To evaluate the proposed index, a LIVE database, which is the most well-known in the area of image quality assessment, is employed and four criteria are used. Experimental results show that the proposed index is more consistent with the other methods.

• Journal of Multimedia Information System
• /
• v.9 no.2
• /
• pp.75-86
• /
• 2022

The existing image quality assessment (IQA) datasets have a small number of samples. Some methods based on transfer learning or data augmentation cannot make good use of image quality-related features. A No Reference (NR)-IQA method based on multi-task training and quality awareness is proposed. First, single or multiple distortion types and levels are imposed on the original image, and different strategies are used to augment different types of distortion datasets. With the idea of weak supervision, we use the Full Reference (FR)-IQA methods to obtain the pseudo-score label of the generated image. Then, we combine the classification information of the distortion type, level, and the information of the image quality score. The ResNet50 network is trained in the pre-train stage on the augmented dataset to obtain more quality-aware pre-training weights. Finally, the fine-tuning stage training is performed on the target IQA dataset using the quality-aware weights to predicate the final prediction score. Various experiments designed on the synthetic distortions and authentic distortions datasets (LIVE, CSIQ, TID2013, LIVEC, KonIQ-10K) prove that the proposed method can utilize the image quality-related features better than the method using only single-task training. The extracted quality-aware features improve the accuracy of the model.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
• /
• 2016.05a
• /
• pp.271-274
• /
• 2016

CT quality assurance imaging evaluation and enforcement as quantitative assessment by phantom image evaluation, assessment items include There are also contrasting the water attenuation coefficient, uniformity, noise, resolution, spatial resolution, 10mm slice thickness evaluation, contrast resolution, space for the resolution, the slice thickness evaluation, it is possible to estimate the error due to the evaluation by the subjective judgment of the tester, using a subjective error image processing program to be computed to minimize the objective evaluation. Basic recording conditions of the CT image quality control assessment is the same as special medical equipment quality control checks, the images were evaluated quantitatively using IMAGE J. For a CT attenuation coefficient, the uniformity, noise evaluation, were evaluated as CT quality control image the standard deviation of the measured value of the digital processing of image smaller and less noise uniform images than the, contrast and resolution assessment is the size of the diameter of a circle having a large the 1 inch, 0.75 inch, 0.5 inch quality if the diameter of the circle, was evaluated in the small circle in the near circle ellipse. Spatial resolution is evaluated by using a self-extracting features of an image processing program, all of the groups of members comprising the acceptance criteria to automatically extract, was evaluated to be very useful for the quantitative assessment. When CT image quality control assessment on the basis of the results such as the above, if using an image processing program to minimize the subjective judgment of the error evaluator and is determined more efficient than would be made quantitative evaluation.

• Journal of the Korean Institute of Landscape Architecture
• /
• v.25 no.3
• /
• pp.101-110
• /
• 1997

The purpose of this study was on suggesting what is the image and image formation factor of interior landscaping plants. For this purpose, the sixty interior landscaping plants were selected. Selected plants were classified into 9 groups by similar characteristics of plants, for example, leaf color and leaf pattern. Data analysis were performed by semantic differential scale method, mean score and multiple regression algorithm. The results are as follows, 1. Comparing with image assessment, group 9 got the highest score in all aspects. 2. Comparing with the image assessment of interior landscaping plants, the "impressive" image was obtained the highest score and "bright", "cool", "beautiful" and "fresh" were followed. 3. Multiple regression analysis was performed to clarify influence degree of the adjectives related to the beauty. The next adjectives were significant check points on assessing the beauty of interior landscaping plants. Also, Guzmania magnifical was investigated to have the most beautiful image with the results of preference analysis. Vriesea splendens, Cordyline terminalis Kunth 'Lilliput' and Peperomia sandersii were identified as considerably preferred plants. were identified as considerably preferred plants.

• Current Optics and Photonics
• /
• v.5 no.5
• /
• pp.514-523
• /
• 2021

Methods for absolute depth estimation have received lots of interest, and most algorithms are concerned about how to minimize the difference between an input defocused image and an estimated defocused image. These approaches may increase the complexity of the algorithms to calculate the defocused image from the estimation of the focused image. In this paper, we present a new method to recover depth of scene based on a sharpness-assessment algorithm. The proposed algorithm estimates the depth of scene by calculating the sharpness of deconvolved images with a specific point-spread function (PSF). While most depth estimation studies evaluate depth of the scene only behind a focal plane, the proposed method evaluates a broad depth range both nearer and farther than the focal plane. This is accomplished using an asymmetric aperture, so the PSF at a position nearer than the focal plane is different from that at a position farther than the focal plane. From the image taken with a focal plane of 160 cm, the depth of object over the broad range from 60 to 350 cm is estimated at 10 cm resolution. With an asymmetric aperture, we demonstrate the feasibility of the sharpness-assessment algorithm to recover absolute depth of scene from a single defocused image.

• Journal of the Institute of Electronics Engineers of Korea SP
• /
• v.45 no.3
• /
• pp.37-45
• /
• 2008

Quality assessment ai s to evaluate if a distorted image or video has a good quality by measuring the difference between the original and distorted images or videos. In this paper, to assess the visual qualify of a distorted image or video, visual features of the distorted image are compared with those of the original image instead of the direct comparison of the distorted image with the original image. We use edge projections from two images as features, where the edge projection can be easily obtained by projecting edge pixels in an edge map along vertical/horizontal direction. In this paper, edge projections are obtained by using vertical/horizontal directions of gradients as well as the magnitude of each gradient. Experimental results show the effectiveness of the proposed quality assessment through the comparison with conventional quality assessment algorithms such as structural similarity(SSIM), edge peak signal-to-noise ratio(EPSNR), and edge histogram descriptor(EHD) methods.

• Journal of the Korean Society for Precision Engineering
• /
• v.15 no.4
• /
• pp.44-50
• /
• 1998

The creep life fraction can be evaluated by the degree of grain deformation since the grain of Cr-Mo base metal deforms in the direction of stress. The grain deformation method using image processing technique is developed for life assessment of in-service high-temperature components. The new assessment model of grain deformation method is presented to apply to in-service components and is verified by interrupted creep test for ex-serviced material of 1Cr-0.5Mo steel. The proposed model, which is irrespective of stress direction, is to evaluate mean of the absolute deviation for the measured ratios which are diametrical maximum to minimum dimensions for grains.

• Journal of radiological science and technology
• /
• v.30 no.4
• /
• pp.427-433
• /
• 2007

This study was conducted to investigate the usefulness of PASL image technique through visual and quantitative assessment by dividing CBF image, conventional perfusion magnetic resonance image, anterior cerebral artery, middle cerebral artery and posterior cerebral artery into 6 territories both right and left in moyamoya disease. In visual assessment, the scope of decreased perfusion in the PASL CBF image and conventional perfusion MR CBF image agreed with the position of deficiency in the MR image. The quantitative assessment, showed that the scope and position of decreased perfusion accord with both in the PASL CBF image and the existing conventional perfusion MR CBF image but the assessment of measuring the quantity of perfusion according to signal intensity showed a little difference.

• Proceedings of the KSRS Conference
• /
• 1999.11a
• /
• pp.433-438
• /
• 1999

The land use type of Korea is high-density. So, the image classification using coarse resolution satellite image may not provide land cover classification results as good as expected. The purpose of this paper is to compare the result of visual interpretation with that of digital image classification of 20 m resolution SPOT satellite image at Kwangju-eup, Kyunggi-do, Korea. Classes are forest, cultivated field, pasture, water and residential area, which are clearly discriminated in visual interpretation. Maximum likelihood classifier was used for digital image classification. Accuracy assessment was done by comparing each classification result with ground truth data obtained from field checking. The classification result from the visual interpretation presented an total accuracy 9.23 percent higher than that of the digital image classification. This proves the importance of visual interpretation for the area with high density land use like the study site in Korea.