• KSII Transactions on Internet and Information Systems (TIIS)
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• v.6 no.1
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• pp.426-445
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• 2012

Conventional rate control (RC) schemes for H.264 video coding usually regulate output bit rate to match channel bandwidth by adjusting quantization parameter (QP) at fixed full frame rate, and the passive frame skipping to avoid buffer overflow usually occurs when scene changes or high motions exist in video sequences especially at low bit rate, which degrades spatial-temporal quality and causes jerky effect. In this paper, an active content adaptive frame skipping scheme is proposed instead of passive methods, which skips subjectively trivial frames by structural similarity (SSIM) measurement between the original frame and the interpolated frame via motion vector (MV) copy scheme. The saved bits from skipped frames are allocated to coded key ones to enhance their spatial quality, and the skipped frames are well recovered based on MV copy scheme from adjacent key ones at the decoder side to maintain constant frame rate. Experimental results show that the proposed active SSIM-based frameskip scheme acquires better and more consistent spatial-temporal quality both in objective (PSNR) and subjective (SSIM) sense with low complexity compared to classic fixed frame rate control method JVT-G012 and prior objective metric based frameskip method.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.46 no.3
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• pp.75-88
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• 2009

This paper presents a video quality assessment method based on quality degradation factors of real-time multimedia streaming services. The video quality degradation is caused by video source compression and network states. In this paper, we propose a blocky metric on an image domain to measure quality degradation by video compression. In this paper, the proposed boundary strength index for the blocky metric is defined by ratio of the variation of two pixel values adjacent to $8{\times}8$ block boundary and the average variation at several pixels adjacent to the two boundary pixels. On the other hand, unnatural image movement caused by network performance deterioration such as jitter and delay factors can be observed. In this paper, a temporal-Jerkiness measurement method is proposed by computing statistics of luminance differences between consecutive frames and play-time intervals between frames. The proposed final Perceptual Video Quality Metric (PVQM) is proposed by consolidating both blocking strength and temporal-jerkiness. To evaluate performance of the proposed algorithm, the accuracy of the proposed algorithm is compared with Difference of Mean Opinion Score (DMOS) based on human visual system.

• Korean Journal of Remote Sensing
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• v.32 no.1
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• pp.49-60
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• 2016

This paper presents a set of methods to evaluate the image quality of smartphone cameras as compared with that of a DSLR camera. In recent years, smartphone cameras have been used broadly for many purposes. As the performance of smartphone cameras has been enhanced considerably, they can be considered to be used for precise mapping instead of metric cameras. To evaluate the possibility, we tested the quality of one DSLR camera and 3 smartphone cameras. In the first step, we compare the amount of lens distortions inherent in each camera using camera calibration sheet images. Then, we acquired target sheet images, extracted reference lines from them and evaluated the geometric quality of smartphone cameras based on the amount of errors occurring in fitting a straight line to observed points. In addition, we present a method to evaluate the radiometric quality of the images taken by each camera based on planar fitting errors. Also, we propose a method to quantify the geometric quality of the selected camera using edge displacements observed in target sheet images. The experimental results show that the geometric and radiometric qualities of smartphone cameras are comparable to those of a DSLR camera except lens distortion parameters.

• Journal of Broadcast Engineering
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• v.13 no.6
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• pp.849-858
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• 2008

In this paper, a no-reference perceptual metric is proposed for image quality assessment. It measures the amount of overall blockiness and blurring of the image and evaluates the amount of ringing, staircase, and mosaic noises around the strong edges. Finally, the individual scores are combined by a fuzzy integral to generate the final quality score of the image. The quality scores obtained by the proposed algorithm show strong relationship with the MOS(Mean Opinion Score) values by experts.

• Nuclear Engineering and Technology
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• v.53 no.7
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• pp.2341-2347
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• 2021

Because single-photon emission computed tomography (SPECT) is one of the widely used nuclear medicine imaging systems, it is extremely important to acquire high-quality images for diagnosis. In this study, we designed a super-resolution (SR) technique using dense block-based deep convolutional neural network (CNN) and evaluated the algorithm on real SPECT phantom images. To acquire the phantom images, a real SPECT system using a^99mTc source and two physical phantoms was used. To confirm the image quality, the noise properties and visual quality metric evaluation parameters were calculated. The results demonstrate that our proposed method delivers a more valid SR improvement by using dense block-based deep CNNs as compared to conventional reconstruction techniques. In particular, when the proposed method was used, the quantitative performance was improved from 1.2 to 5.0 times compared to the result of using the conventional iterative reconstruction. Here, we confirmed the effects on the image quality of the resulting SR image, and our proposed technique was shown to be effective for nuclear medicine imaging.

• Journal of Electrical Engineering and Technology
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• v.10 no.3
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• pp.1233-1243
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• 2015

The contrast enhancement is great challenge in the image processing when images are suffering from poor contrast problem. Therefore, in order to overcome this problem an automatic method is proposed for contrast enhancement of natural color images. The proposed method consist of two stages: in first stage lightness component in YIQ color space is normalized by sigmoid function after the adaptive histogram equalization is applied on Y component and in second stage automatic color contrast enhancement algorithm is applied on output of the first stage. The proposed algorithm is tested on different NASA color images, hyperspectral color images and other types of natural color images. The performance of proposed algorithm is evaluated and compared with the other existing contrast enhancement algorithms in terms of colorfulness metric and color enhancement factor. The higher values of colorfulness metric and color enhancement factor imply that the visual quality of the enhanced image is good. Simulation results demonstrate that proposed algorithm provides higher values of colorfulness metric and color enhancement factor as compared to other existing contrast enhancement algorithms. The proposed algorithm also provides better visual enhancement results as compared with the other existing contrast enhancement algorithms.

• Journal of Korean Society for Quality Management
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• v.44 no.3
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• pp.527-540
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• 2016

Purpose: The purpose of this study was to propose contrast metric which is based on the human visual perception and thus it can be used to improve the quality of digital images in many applications. Methods: Previous literatures are surveyed, and then the proposed method is modeled based on Human Visual System(HVS) such as multiscale property of the contrast sensitivity function (CSF), contrast constancy property (suprathreshold), color channel property. Furthermore, experiments using digital images are shown to prove the effectiveness of the method. Results: The results of this study are as follows; regarding the proposed contrast measure of complex images, it was found by experiments that HVS follows relatively well compared to the previous contrast measurement. Conclusion: This study shows the effectiveness on how to measure the contrast of complex images which follows human perception better than other methods.

• Nuclear Engineering and Technology
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• v.55 no.12
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• pp.4591-4596
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• 2023

Single-photon emission computed tomography SPECT image reconstruction methods have a significant influence on image quality, with filtered back projection (FBP) and ordered subset expectation maximization (OSEM) being the most commonly used methods. In this study, we proposed newly-designed adaptive non-blind deconvolution with a structural similarity (SSIM) index that can take advantage of the FBP and OSEM image reconstruction methods. After acquiring brain SPECT images, the proposed image was obtained using an algorithm that applied the SSIM metric, defined by predicting the distribution and amount of blurring. As a result of the contrast to noise ratio (CNR) and coefficient of variation evaluation (COV), the resulting image of the proposed algorithm showed a similar trend in spatial resolution to that of FBP, while obtaining values similar to those of OSEM. In addition, we confirmed that the CNR and COV values of the proposed algorithm improved by approximately 1.69 and 1.59 times, respectively, compared with those of the algorithm involving an inappropriate deblurring process. To summarize, we proposed a new type of algorithm that combines the advantages of SPECT image reconstruction techniques and is expected to be applicable in various fields.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.10 no.1
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• pp.257-271
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• 2016

The Structural SIMilarity (SSIM) index is one of the most widely-used methods for perceptual image quality assessment (IQA). It is based on the principle that the human visual system (HVS) is sensitive to the overall structure of an image. However, it has been reported that indices predicted by SSIM tend to be biased depending on the type of distortion, which increases the deviation from the main regression curve. Consequently, SSIM can result in serious performance degradation. In this study, we investigate the aforementioned phenomenon from a new perspective and review a constant that plays a big role within the SSIM metric but has been overlooked thus far. Through an experimental study on the influence of this constant in evaluating images with SSIM, we are able to propose a new solution that resolves this issue. In the proposed IQA method, we first design a system to classify different types of distortion, and then match an optimal constant to each type. In addition, we supplement the proposed method by adding color perception-based structural information. For a comprehensive assessment, we compare the proposed method with 15 existing IQA methods. The experimental results show that the proposed method is more consistent with the HVS than the other methods.

• The KIPS Transactions:PartB
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• v.15B no.5
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• pp.383-390
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• 2008

Block based transform coding is most popular approach for image and video compression. However it suffers from severe quality degradation especially from blocking artifacts. The subjective quality degradation caused by such blocking artifacts in general does not agree well with an objecive quality measurement such as PSNR. Hence new quality evaluation technique is necessary. We propose a new image quality assessment method by measuring blocking artifacts for block based transform coded images. In order to characterize blocking artifacts, proposed method utilizes the facts that, blocking artifacts, when occur, have different pixel values along the block boundaries and such differences usually continuously span along the whole boundaries. This method does not require the original uncompressed image. It operates on single block boundary and quantifies the amount of blocking artifacts on it. Experiments on various compressed images various bitrates show that proposed quantitative measure of blocking artifacts matches well with the subjective quality of them judged by human visual system.