Sung-Ho Bae
Kyung Hee University · 情報科学
研究室紹介
Professor Sung-Ho Bae's research lab specializes in human visual system (HVS)-inspired image and video processing, with a strong focus on perceptual quality assessment, just-noticeable distortion (JND) modeling, and advanced computer vision applications. The lab develops computational models that integrate visual perception principles—such as contrast sensitivity, luminance adaptation, and masking effects—into practical solutions for image/video compression, quality assessment, and autonomous vehicle systems. Key research directions include structural and perceptual modeling for image quality, real-time LiDAR-based lane detection for high-definition map generation, and efficient coding of ultra-high-definition (4K UHD) video. The lab emphasizes the integration of psychophysical insights with engineering applications to enhance visual fidelity and system efficiency.
Research Overview
Research Output Trend
Figures are computed from collected data and may differ slightly.
Selected Papers
15Computational models for image quality assessment (IQA) have been developed by exploring effective features that are consistent with the characteristics of a human visual system (HVS) for visual quality perception. In this paper, we first reveal that many existing features used in computational IQA methods can hardly characterize visual quality perception for local image characteristics and various distortion types. To solve this problem, we propose a new IQA method, called the structural contra
In this paper, we present the experimental results with analysis for subjective quality assessment on 4K-ultra-high definition (UHD) video encoded by high efficiency video coding (HEVC) to investigate its feasibility for 4K-UHD TV broadcasting services. The subjective quality assessment on the HEVC-encoded (impaired) 4K-UHD video is conducted for the three target bitrates of 18, 23, and 36 Mb/s, the two color formats of YUV420 and YUV444, and the two viewing distances of 0.75 times the height of
The generation of digital maps with lane-level resolution is rapidly becoming a necessity, as semi- or fully-autonomous driving vehicles are now commercially available. In this paper, we present a practical real-time working prototype for road lane detection using LiDAR data, which can be further extended to automatic lane-level map generation. Conventional lane detection methods are limited to simple road conditions and are not suitable for complex urban roads with various road signs on the gro
In image and video processing fields, Discrete Cosine Transform (DCT)-based just-noticeable difference (JND) profiles have effectively been utilized to remove perceptual redundancies in pictures for compression. In this paper, we solve two problems that are often intrinsic to the conventional DCT-based JND profiles: 1) no foveated masking (FM) JND model has been incorporated in modeling the DCT-based JND profiles and 2) the conventional temporal masking (TM) JND models assume that all moving obj
Many conventional DCT based Just Noticeable Distortion (JND) models incorporate luminance adaptation (LA) effect of the human visual system (HVS). The conventional LA-JND models exploit only background luminance to estimate JND values. In this letter, we reveal that the LA effect of HVS depends not only on background luminance but also on frequency in DCT domain. In addition, we first propose a novel DCT-based LA-JND model that takes into account its frequency characteristics. From our psychophy
In this paper, we propose a new DCT-based just noticeable difference (JND) profile incorporating the spatial contrast sensitivity function, the luminance adaptation effect, and the contrast masking (CM) effect. The proposed JND profile overcomes two limitations of conventional JND profiles: 1) the CM JND models in the conventional JND profiles employed simple texture complexity metrics, which are not often highly correlated with perceived complexity, especially for unstructured patterns. So, we
Discrete Cosine Transform (DCT)-based just noticeable difference (JND) profiles have widely been applied into human perception-based video coding in order to reduce perceptual redundancy, which is one of the main goals of perceptual video coding (PVC). However, there are two problems for this approach: 1) the JND value of each transform coefficient is estimated for a fixed-sized DCT kernel (e.g., 8 × 8), but flexible coding structures with variable-sized transform units have been utilized in sta
Condensing heat transfer ratesInside a horizontal tube were Investigated for large quality changes across the tube.The proposed correlation is a modification of the work of Rohsenow, Webber and Ling [29].The result of the investigation is modified through new variables which include the effect of the true axial pressure gradient in a tube.Experimental data are presented for a range of flow conditions.A 0.493 in.ID, 19.75 ft.long nickel tube was used for condensing Refrigerant-12.The saturation t
Just noticeable distortion (JND) refers to minimum distortion threshold, which is visible to human visual system. JND models are applicable to various image and video processing fields, especially perceptual video compression fields by eliminating perceptually redundant amounts of signal components. The conventional DCT-based JND models for contrast masking (CM) effects simply reflect the texture complexity of images into a limited number of texture complexity categories. So such JND models are
Recent development of computational image quality assessment methods has shown to give very promising results in measuring perceptual visual quality for distorted images. However, most of them are difficult to be applied for optimization problems due to the lack of desirable mathematical properties, such as differentiability, convexity, and valid distance metricability. This paper proposes a novel Discrete Cosine Transform (DCT)-based quality degradation metric, called DCT-QM, which is based on
The Structural SIMimarity (SSIM) is one of the most prominent image quality assessment (IQA) methods due to its high prediction performance and wide applicability for image quality optimization problems. To reflect the luminance adaptation (LA) characteristic of human visual system (HVS), SSIM is modelled to have high consistency with Weber's law. However, it inevitably has some intrinsic faults that wrongly incorporate the LA effect into SSIM. In this paper, we firstly analyze that Weber's law
To realize the Internet of Things (IoT), an important step would be to allow things and information about them to be accessible in an easy way and a platform-independent manner. As World Wide Web (WWW, Web) shows explosive growth over the last decade, the web is the most popular and familiar user interface to acquire knowledge about everyday life. In this respect, web browsing through standard protocols like HTTP over TCP could be a promising approach to access things and to collect things' info
Studies on human visual systems (HVS) recommend that assessing the structural information change can provide a good approximation of the perceived image quality. Moreover, psychovisual studies have suggested that the HVS is more sensitive to the extraction of visual information in the salient regions rather than minor regions. Based on the above observations, this Letter proposes a novel full‐reference image quality assessment metric called the visual saliency based structural contrast‐quality i