Chul-Hee Lee
Yonsei University · Computer Science
About the Lab
Professor Chul-Hee Lee's research lab specializes in video and image quality assessment, with a strong focus on modeling human visual perception for objective quality measurement. The lab develops advanced signal processing techniques—such as wavelet transforms, edge-aware analysis, and spatiotemporal frequency modeling—to create accurate, perceptually aligned video quality metrics. Key research directions include objective video quality assessment, image resizing with perceptual fidelity, and robust moving object detection in complex video environments. The lab also contributes to standardization efforts through subjective testing methodologies and their integration with objective models.
Research Overview
Research Output Trend
Figures are computed from collected data and may differ slightly.
Selected Papers
15We propose a new method for an objective measurement of video quality. By analyzing subjective scores of various video sequences, we find that the human visual system is particularly sensitive to degradation around edges. In other words, when edge areas of a video sequence are degraded, evaluators tend to give low quality scores to the video, even though the overall mean squared error is not large. Based on this observation, we propose an objective video quality measurement method based on degra
The standard interpolation approach to image resizing is to fit the original picture with a continuous model and resample the function at the desired rate. However, one can obtain more accurate results if one applies a filter prior to sampling, a fact well known from sampling theory. The optimal solution corresponds to an orthogonal projection onto the underlying continuous signal space. Unfortunately, the optimal projection prefilter is difficult to implement when sine or high order spline func
We propose a new method for objective measurement of video quality using the wavelet transform. We exploit the fact that the sensitivity of the human visual system varies over spatiotemporal frequencies. First, in order to compute spatial-frequency components, the wavelet transform is applied to each frame of source and processed videos. Then, the difference (squared error) of the wavelet coefficients in each subband is computed and summed, producing a difference vector for each frame. By repeat
As adaptive bit-streaming services are increasingly available, there is a greater need to monitor the perceptual quality of video services of networks. A number of objective video/image quality measurement methods have been proposed. In this paper, we performed several subjective tests using UHD signals that are reduced to various resolutions. Then, we applied several objective video/image quality measurement models to investigate the applicability of such models to monitor the perceptual qualit
Robust detection of moving objects from video sequences is an important task in machine vision systems and applications. To detect moving objects, accurate background subtraction is essential. In real environments, due to complex and various background types, background subtraction is a challenging task. In this paper, we propose a pixel-based background subtraction method based on spatial similarity. The main difficulties of background subtraction include various background changes, shadows, an
In this paper, we present comparison of three subjective testing methods: the double stimulus continuous quality scale (DSCQS) method, the single stimulus continuous quality evaluation (SSCQE) method and the absolute category rating (ACR) method. The DSCQS method was used for validate objective models in the VQEG Phase II FRTV test. The SSCQE method is chosen to be used in the VQEG RRTV test. The ACR method is chosen to be used in the VQEG Multimedia test. Since a different subjective test metho
We propose a new algorithm to find the corpus callosum automatically from midsagittal brain MR (magnetic resonance) images using the statistical characteristics and shape information of the corpus callosum. We first extract regions satisfying the statistical characteristics (gray level distributions) of the corpus callosum that have relatively high intensity values. Then we try to find a region matching the shape information of the corpus callosum. In order to match the shape information, we pro
Research Areas
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