Taeuk Jeong

Objective video quality assessment

We 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 degradation around edges. In the proposed method, we first apply an edge detection algorithm to videos and locate edge areas. Then, we measure degradation of those edge areas by computing mean squared errors and use it as a video quality metric after some postprocessing. Experiments show that the proposed method significantly outperforms the conventional peak signal-to-noise ratio (PSNR). This method was also independently evaluated by independent laboratory groups in the Video Quality Experts Group (VQEG) Phase 2 test. The method consistently provided good performances. As a result, the method was included in international recommendations for objective video quality measurement.

Deinterlacing with selective motion compensation

We propose a new deinterlacing algorithm with selective motion compensation. It has been reported that deinterlacing methods using motion compensation produce significantly improved results, although they tend to yield undesired results in fast moving areas. This is due to weak correlations between the previous and current frames. The proposed algorithm solves this problem by selectively applying motion-compensated deinterlacing. We first apply intrafield interpolation in the spatial domain, and then selectively apply motion compensations according to the type of motion vectors. Experimental results show that the proposed method produces noticeably improved performance compared to existing motion-compensated deinterlacing methods.

Hybrid Compression of Hyperspectral Images Based on PCA With Pre-Encoding Discriminant Information

It has been shown that image compression based on principal component analysis (PCA) provides good compression efficiency for hyperspectral images. However, PCA might fail to capture all the discriminant information of hyperspectral images, since features that are important for classification tasks may not be high in signal energy. To deal with this problem, we propose a hybrid compression method for hyperspectral images with pre-encoding discriminant information. A feature extraction method is first applied to the original images, producing a set of feature vectors that are used to generate feature images and then residual images by subtracting the feature-reconstructed images from the original ones. Both feature images and residual images are compressed and transmitted. Experiments on data from the Airborne Visible/Infrared Imaging Spectrometer sensor indicate that the proposed method provides better compression efficiency with improved classification accuracy than conventional compression methods.

Edge-adaptive Demosaicking for Artifact Suppression Along Line Edges

In this paper, we present an edge-adaptive demosaicking method for artifact suppression along line edges. Although various demosaicking methods have been proposed, they still suffer from artifacts along line edges. In order to suppress these undesirable artifacts, the proposed method first determines the pattern of line edges and interpolates missing pixels along the detected direction during initial interpolation. Then, refinement and calibration processes are carried out in series to improve image quality. Experimental results demonstrate that the proposed method produces visually pleasing images and outperforms existing demosaicking methods in terms of the peak signal-to-noise ratio (PSNR) and the mean squared error of S-CIELAB.

No-reference image-quality metric based on blur radius and visual blockiness

We propose new blur and blocking metrics and then present a no-reference image-quality assessment method using these blur and blocking metrics. To compute the blur metric, we first estimated a blur radius from a given image and its reblurred version by using edge differences and edge amplitudes. Because blurring in edge regions is generally more sensitive to human perception, the blur metric was estimated from the edge blocks. We also used kurtosis and structural similarity to better estimate the blur metric. To compute the blocking metric, the blocking artifact was modeled as a 2-D step function and the blockiness visibility was estimated by the brightness difference between adjacent blocks. After the blocky position was determined, the blocking metric was computed from the six differences between four adjacent blocks. Experimental results show that the objective quality scores correlated highly with the subjective quality scores.

Edge-dependent deinterlacing using weighted motion estimation

We propose a new edge-dependent deinterlacing method using weighted motion estimation. Motion-compensated methods use the motion information and produce improved performance. However, they still tend to produce unsatisfactory picture quality in rapid motion areas and edge regions due to incorrect motion estimation. The proposed method mitigates these problems by limiting motion estimation to an appropriate search range and applying a weighted edge motion estimation with a piecewise linear weight function. Experimental results show that the proposed method provides noticeable improvement in terms of the peak signal-to-noise ratio and produces better picture quality in edge regions.

Compression of hyperspectral images with discriminant features enhanced

In this paper, we propose two compression methods for hyperspectral images with discriminant features enhanced. Generally, when hyperspectral images are compressed with conventional image compression algorithms, which mainly minimize mean squared errors, discriminant features of the original data may not be well preserved since they may not be necessarily large in energy. In this paper, we propose two compression methods that do preserve the discriminant information. In the first method, we enhanced the discriminant features and then compressed the enhanced data using conventional image compression algorithms such as 3D JPEG 2000. In the second method, we applied a feature extraction method and extracted the discriminantly dominant feature vectors. By examining the dominant feature vectors, we determined the discriminant usefulness of each spectral band. Based on these findings, we determined the bit allocation of each spectral band assuming 2D compression methods are used. Experiments show that the proposed methods effectively preserved the discriminant information and yielded improved classification accuracies compared to existing compression algorithms.

Automated Brain Segmentation Algorithm for 3D Magnetic Resonance Brain Images

In this paper, we propose a new brain segmentation method for 3D magnetic resonance (MR) brain images. The proposed method consists of four steps: background rejection, image normalization, initial slice segmentation, and brain segmentation. In the image normalization step, intensity non-uniformity is removed. In the brain segmentation step, we use mathematical morphological operators and masking. The proposed algorithm was tested with twenty 3D MR normal brain image sets. Experiment results showed the proposed algorithm is fast and provides robust and satisfactory results.

Comparison of subjective video quality assessment methods for multimedia applications

In this paper, we compared two subjective assessment methods DSCQS(Double Stimulus Continuous Quality Scale method) and ACR(Absolute Category Rating). These methods are widely used in order to evaluate video quality for multimedia application. We performed subjective quality tests using DSCQS and ACR methods. The subjective scores obtained by the DSCQS and ACR methods show that these methods are highly correlated in terms of MOS(Mean Opinion Score) and have slightly lower correlation in terms of DMOS(Difference Mean Opinion Score). The results indicate that ACR method is an effective subjective quality assessment method, which shows compatible performance with DSCQS method and can evaluate a larger number of video sequences.

Subjective video quality comparison using various displays

Objective video quality measurement has become an important issue, as multimedia services are now widely available over the Internet and other wireless communication media. Traditionally, professional CRT monitors have been used to measure subjective video quality. However, the majority of users have LCD, plasma display panel (PDP), or consumer-graded CRT monitors. We compared the subjective video quality of various TV and LCD PC monitors. Subjective tests were performed with a wide range of video sequences using different monitors, and their correlations were analyzed. Although there were high correlations among the various display monitors, care should be taken in selecting a monitor for certain applications.