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Image Quality Assessment Considering both Computing Speed and Robustness to Distortions
Suk-Won Kim, Seongwoo Hong, Jeong-Chan Jin, Young-Jin Kim
http://doi.org/10.5626/JOK.2017.44.9.992
To assess image quality accurately, an image quality assessment (IQA) metric is required to reflect the human visual system (HVS) properly. In other words, the structure, color, and contrast ratio of the image should be evaluated in consideration of various factors. In addition, as mobile embedded devices such as smartphone become popular, a fast computing speed is important. In this paper, the proposed IQA metric combines color similarity, gradient similarity, and phase similarity synergistically to satisfy the HVS and is designed by using optimized pooling and quantization for fast computation. The proposed IQA metric is compared against existing 13 methods using 4 kinds of evaluation methods. The experimental results show that the proposed IQA metric ranks the first on 3 evaluation methods and the first on the remaining method, next to VSI which is the most remarkable IQA metric. Its computing speed is on average about 20% faster than VSI’s. In addition, we find that the proposed IQA metric has a bigger amount of correlation with the HVS than existing IQA metrics.
Human Visual System-Aware and Low-Power Histogram Specification and Its Automation for TFT-LCDs
Backlight has a major factor in power consumption of TFT-LCDs which are most popular in portable devices. There have been a lot of attempts to achieve power savings by backlight dimming. At the same time, the researches have shown image compensation due to decreased brightness of a displayed image. However, existing image compensation methods such as histogram equalization have some limits in completely satisfying the human visual system (HVS)-awareness. This paper proposes an enhanced dimming technique to obtain both power saving and HVS-awareness by combining pixel compensation and histogram specification for TFT-LCDs. This method executes a search algorithm and an automation algorithm employing simplified calculations for fast image processing. Experimental results showed that the proposed method achieved significant improvement in visual satisfaction per power saving over existing backlight dimming.
Human Visual System-Aware Optimal Power-Saving Color Transformation for Mobile OLED Devices
Jae-Hyeok Lee, Eun-Sil Kim, Young-Jin Kim
Due to the merits of OLED displays such as fast responsiveness, wide view angle, and power efficiency, their use has increased. However, despite the power efficiency of OLED displays, the portion of their power consumption among the total power consumption is still high since user interaction-based applications such as instant messaging, video play, and games are frequently used. Their power consumption varies significantly depending on the display contents and thus color transformation is one of the low-power techniques used in OLED displays. Prior low-power color transformation techniques have not been rigorously studied in terms of satisfaction of the human visual system, and have not considered optimal visual satisfaction and power consumption at the same time in relation to color transformation. In this paper, we propose a novel low-power color transformation technique which strictly considers human visual system-awareness as well as optimization of both visual satisfaction and power consumption in a balanced way. Experimental results show that the proposed technique achieves better human visual satisfaction in terms of visuality and also shows on average 13.4% and 22.4% improvement over a prior one in terms of power saving.
Perceptual Color Difference based Image Quality Assessment Method and Evaluation System according to the Types of Distortion
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.
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