Hyo Kak Kim

Selective inter-layer residual prediction for SVC-based video streaming

The scalable video coding (SVC) standard adopts the inter-layer residual prediction (ILRP) algorithm to encode the residual signal of the enhancement layer (EL). The ILRP reduces the number of bits required for encoding the residual signal but incurs excessive encoding time. In this paper, we propose a fast encoding algorithm for SVC-based video streaming. In this algorithm, the ILRP is selectively applied to the coding modes depending on their Lagrangian rate-distortion costs. Experimental results show that the proposed algorithm can reduce computational complexity with negligible video quality degradation and bitrate increments.

Spatial color histogram based center voting method for subsequent object tracking and segmentation

In this paper, we introduce an algorithm for object tracking in video sequences. In order to represent the object to be tracked, we propose a new spatial color histogram model which encodes both the color distribution and spatial information. Using this spatial color histogram model, a voting method based on the generalized Hough transform is employed to estimate the object location from frame to frame. The proposed voting based method, called the center voting method, requests every pixel near the previous object center to cast a vote for locating the new object center in the new frame. Once the location of the object is obtained, the back projection method is used to segment the object from the background. Experiment results show successful tracking of the object even when the object being tracked changes in size and shares similar color with the background.

An efficient POCS-based post-processing technique using wavelet transform in HDTV

In this paper, we propose a new post-processing method, based on the theory of the projection onto convex sets (POCS) to reduce the in digital high definition television (HDTV) decoded images. We propose a new smoothness constraint set (SCS) and its projection operator in the wavelet transform (WT) domain to remove unnecessary high-frequency components caused by blocking artifacts. We also propose a new method to find and preserve the original high frequency components of the image edge. Experimental results show that the proposed method can not only achieve a significantly enhanced subjective quality, but also exhibit the PSNR improvement in the output image.

A content-aware image stitching algorithm for mobile multimedia devices

This paper presents a new image stitching algorithm for mobile multimedia devices. In general, the stitched image obtained by optimal seam finding can avoid the ghost effects but sometimes experience the unconformity of the image structure and color differences near the transition region between images. To solve these problems, the proposed algorithm adopts the seam carving/inserting operator and exploits an adaptive color blending algorithm. Experimental results show that the proposed algorithm can produce a stitched image without visible artifacts including the ghost effects, the unconformity of the structure, and the visible color differences. With respect to the visual quality of the stitched images, the proposed algorithm significantly outperforms simple conventional stitching algorithms and is comparable to the computationally demanding Poisson image editing algorithm1.

Kernel-Based Structural Binary Pattern Tracking

In this paper, we propose a new pattern model, called the structural binary pattern (SBP) model, for object tracking. For the proposed SBP model, we introduce an alternate thresholding scheme to generate a set of multiple SBPs. The SBP encodes not only the binary pattern consisting of binarized differences between the average intensities of subregions within the target region, but also the spatial configuration of the subregions. With the proposed SBP model, we define a metric for similarity between the SBP models from the target and candidate for target localization, which is based on an isotropic kernel weighted Hamming distance. To further improve the tracking performance, we employ a color-based tracking method along with the SBP-based tracking method. The experimental results show that the proposed algorithm exhibits the better performance even when the object being tracked confronts drastic illumination changes, partial occlusion, a similar colored background, or low illumination as compared with conventional tracking methods.

An effective pedestrian detection method for driver assistance system

A driver assistance system typically adopts the wide-angle camera to obtain a wide-view image. However, the wide-angle camera often produces radial distortion. Since the conventional training-based pedestrian detection method uses distortion-free training samples, it is not suitable for distorted images. In this paper, we propose an effective pedestrian detection method that divides pedestrian training samples into several classes according to the amount of radial distortion, and trains each class separately. Likewise, a test image is divided into sub-regions and detection is performed for each sub-region separately. Experimental results show that our approach provides better performance compared to the conventional method.

Efficient SVC encoding scheme for the video transmission over various networks

In this paper, we propose an efficient encoding scheme in scalable video coding (SVC) for the video transmission over various networks. In order to improve the encoding efficiency, we adopt the spatio-temporal motion vector (MV) prediction method in the motion compensated temporal filtering (MCTF). Experimental results show that the proposed method outperforms the conventional method in terms of compression ratio.

Improved motion vector prediction method in scalable video coding

This paper presents an improved motion vector (MV) prediction scheme in scalable video coding (SVC) to increase coding efficiency. In order to improve the accuracy of prediction, we adopt the spatio-temporal prediction. Experimental results show that the proposed method outperforms the conventional prediction methods.

Spatial deblocking algorithm based on human visual system

We introduce a new deblocking algorithm that can remove the blockiness without blurring image details. After modeling the blocking artifacts as well as the real edges, the proposed algorithm controls the suppression of the blocking artifact based on the human visual system. Through simulations, we show that our algorithm can successfully reduces the blocking artifacts without excessive blurring.

Improved POCS-based deblocking technique using wavelet transform in block coded image

This paper presents a improved POCS-based deblocking technique, based on the theory of the projection onto convex sets (PODS) to reduce the blocking artifacts in decoded images. We propose a new smoothness constraint set (SCS) and its projection operator in the wavelet transform (WT) domain to remove unnecessary high-frequency components caused by blocking artifacts. In order to eliminate the blocking artifacts component while preserving the original edge component, we also propose a significant coefficient decision method (SCDM)for fast and efficient performance. Experimental results show that the proposed method can not only achieve a significantly enhanced subjective quality but also increase the PSNR improvement in the reconstructed image.