• Journal of the Semiconductor & Display Technology
• /
• v.9 no.4
• /
• pp.57-61
• /
• 2010

The wavelet transform was adopted as the transform for JPEG2000. However, wavelet has weakness about smoothness along the contours and limited directional information. So we use to other transform, called contourlet transform in compression. Objective quality assessment methods currently used Peak signal to noise Ratio(PSNR). But that is not very well matched to perceived visual quality. So new image quality assessment is required. In this paper, we propose a new method for image compression based on the contourlet transform, which has been recently introduced. In addition we evaluated compression image quality using PSNR and SSIM. Finally contourlet transform has a good result about images with smooth contours and SSIM is good method for image evaluation compared to PSNR.

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

• The Journal of the Korea Contents Association
• /
• v.11 no.8
• /
• pp.68-76
• /
• 2011

Due to the high interests of image quality by consumers, the concerned market becomes more heated. Recent digital camera development tendency shows to perform the higher image quality to meet consumers demand of quality satisfaction. However it is hard to confirm that development of objective image quality performance means positive subjective image quality preference. And also, we cannot find out the previous researches concerned on correlation between objective and subjective image quality comparison. Therefore, it is necessary to analyze the consumers preferred images based on objective image quality performance. Throughout this paper, we analyze statistical correlation between the objective and subjective image quality assessment methods by using ISO standards. In these results, we try to find attributes that enhance image quality. We suggest not only to analyze and reflect on customers' preferences, but also to pursue the high quality image performance practically. We expect the results of this paper to positively influence product development.

• Journal of Broadcast Engineering
• /
• v.16 no.3
• /
• pp.552-560
• /
• 2011

In this paper, a new no-reference sharpness measure using frequency domain coefficients is proposed. Although most existing sharpness measures used pixel intensity to compute the blur degree, the proposed sharpness measure computes the sharpness using frequency coefficients. To assess the perceived sharpness of a given image, the image is re-blurred by a Gaussian low pass filter and a new quality measure function was defined using the frequency domain coefficients of the given image and the re-blurred image. To evaluate the proposed algorithms, TID2008 quality assessment database was used. Experimental results show that the proposed quality assessment method showed high correlation with the subjective scores.

• 한국정보디스플레이학회:학술대회논문집
• /
• 2006.08a
• /
• pp.660-664
• /
• 2006

A novel quantified method based on requirement analysis of image quality to improve display image quality was proposed. Nowadays, the image quality was limited by the poor understanding of the image quality requirement, which led to the critical factors of image quality could not be controlled during display development. Our method was set up to resolve this problem by clarifying the relationship between the image quality level and the effect factors in image processing. Moreover, the subjective factors were eliminated extremely by the image quality quantification. The method was applied in the RPTV development life cycle and its efficiency was demonstrated.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.14 no.10
• /
• pp.4060-4079
• /
• 2020

Blur is an important type of image distortion. How to evaluate the quality of blurred image accurately and efficiently is a research hotspot in the field of image processing in recent years. Inspired by the multi-scale perceptual characteristics of the human visual system (HVS), this paper presents a no-reference image blur/sharpness assessment method based on multi-scale local features in the spatial domain. First, considering various content has different sensitivity to blur distortion, the image is divided into smooth, edge, and texture regions in blocks. Then, the Gaussian scale space of the image is constructed, and the categorized contrast features between the original image and the Gaussian scale space images are calculated to express the blur degree of different image contents. To simulate the impact of viewing distance on blur distortion, the distribution characteristics of local maximum gradient of multi-resolution images were also calculated in the spatial domain. Finally, the image blur assessment model is obtained by fusing all features and learning the mapping from features to quality scores by support vector regression (SVR). Performance of the proposed method is evaluated on four synthetically blurred databases and one real blurred database. The experimental results demonstrate that our method can produce quality scores more consistent with subjective evaluations than other methods, especially for real burred images.

• Computers and Concrete
• /
• v.10 no.4
• /
• pp.409-417
• /
• 2012

The quality of high performance concrete largely depends on water cement ratio, porosity, material composition and mix methods. The uniformity of color, texture and compressive strengths are quality indicators commonly used to assess the overall characteristics of concrete mixes. The homogeneity and share of coarse aggregates play a key role in concrete quality and must be analyzed in a microscopic point of view. This research studies the quality of high performance concrete by taking drilled cores in both horizontal and vertical directions from a 1.0 $m^3$ specimen. The coarse aggregate, expressed in digitized $100{\times}116$ dpi resolution images are processed based on brightness in colors through commercial software converted into text files. With the image converting to text format, the share of coarse aggregate is quantified leading to a satisfactory assessment of homogeneity - a quality index of high performance concrete. The compressive strengths of concrete and the shares of coarse aggregate of the samples are also compared in this research study to illustrate its correlation in concrete quality. It is concluded that a higher homogeneity of aggregate exists in the vertical plane than that of the horizontal planes of the high performance concrete. In addition, the concrete specimen showing denser particle packing has relatively higher compressive strengths. The research methodology provides an easy-to-use, direct measurement of high performance concrete when conducting quality assessment in the construction site.

• 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.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.14 no.7
• /
• pp.2938-2956
• /
• 2020

Objective image quality assessment (IQA) models have been developed by effective features to imitate the characteristics of human visual system (HVS). Actually, HVS is extremely sensitive to color degradation and complex texture changes. In this paper, we firstly reveal that many existing full reference image quality assessment (FR-IQA) methods can hardly measure the image quality with contrast and masking texture changes. To solve this problem, considering texture masking effect, we proposed a novel FR-IQA method, called Texture and Color Quality Index (TCQI). The proposed method considers both in the masking effect texture and color visual perceptual threshold, which adopts three kinds of features to reflect masking texture, color difference and structural information. Furthermore, random forest (RF) is used to address the drawbacks of existing pooling technologies. Compared with other traditional learning-based tools (support vector regression and neural network), RF can achieve the better prediction performance. Experiments conducted on five large-scale databases demonstrate that our approach is highly consistent with subjective perception, outperforms twelve the state-of-the-art IQA models in terms of prediction accuracy and keeps a moderate computational complexity. The cross database validation also validates our approach achieves the ability to maintain high robustness.

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