Jong-Ho Yun

Design of an Image Interpolator for Low Computation Complexity

In this paper, we propose an image interpolator for low computational complexity. The proposed image interpolator supports the image scaling using a modified cubic convolution interpolation between the input and output resolutions for a full screen display. In order to reduce the computational complexity, we use the difference in value of the adjacent pixels for selecting interpolation methods and linear function of the cubic convolution. The proposed image interpolator is compared with the conventional one for the computational complexity and image quality. The proposed image interpolator has been designed and verified by Verilog HDL(Hardware Description Language). It has been synthesized using the Xilinx VirtexE FPGA, and implemented using an FPGA-based prototype board.

An image enhancement algorithm for detecting the license plate region using the image of the car personal recorder

.Abstract We propose an adaptive histogram stretching algorithm for application to a cars personal recorder. The algorithm was used for pre-processing to detect the license plate region in an image from a personal recorder. Thealgorithm employs a Probability Density Function (PDF) and Cumulative Distribution Function (CDF) to analyze the distribution diagram of the images. These two functions are calculated using an image obtained by sampling at a certain pixel interval. The images were subjected to different levels of stretching, and experiments were done on the images to extract their characteristics. The results show that the proposed algorithm provides less deterioration than conventional algorithms. Moreover, contrast is enhanced according to the characteristics of the image. The algorithmcould provide better performance than existing algorithms in applications for detecting search regions for license plates.Keywords : Cumulative Distribution Function, Histogram Equalization, Image Enhancement, Car Personal Recorder, car license plate

A Road Extraction Algorithm using Mean-Shift Segmentation and Connected-Component

In this paper, we propose a method for extracting a road area by using the mean-shift method and connected-component method. Mean-shift method is very effective to divide the color image by the method of non-parametric statistics to find the center mode. Generally, the feature points of road are extracted by using the information located in the middle and bottom of the road image. And it is possible to extract a road region by using this feature-point and the partitioned color image. However, if a road region is extracted with only the color information and the position information of a road image, it is possible to detect not only noise but also off-road regions. This paper proposes the method to determine the road region by eliminating the noise with the closing / opening operation of the morphology, and by extracting only the portion of the largest area using a connected-components method. The proposed method is simulated and verified by applying the captured road images.

Low Computational Adaptive Expanded Block Search Motion Estimation Method

In this paper, Low Computational Adaptive Expanded Block Search Motion Estimation Method is proposed. Proposed method classifies ME blocks as Average Motion Block(AMB) and Local Motion Block(LMB) according to correlation of reference frame. It could reduce the computational complexity with performing Modified Fast Search(MFS). And accuracy of MV is also increased by 4 sub-blocks on LMB and Block Expansion(BE). The experimental results show that the proposed method has better performance that increased 1.8dB than Diamond Search and 0.6dB than Full Search with 7.5 % computation of Full Search. The proposed method could be applied to video compression and Frame Rate Conversion(FRC).

An Adaptive Contrast Enhancement Method by Histogram Compensation

Histogram Equalization(HE) is one of the well known methods for contrast enhancement. but, it did not applied directly due to side effects such as significant change in brightness or washed out appearance. Many conventional method try to overcome this problem but they did not guarantee various image or depend on user define parameter. In this paper, an Adaptive histogram Compensated Histogram Equalization(ACHE) is proposed for contrast enhancement. ACHE has a parameter that based on median of input image. Histogram of input image is compensated according to parameter. And then finally compensated histogram is equalized. Experimental results show that proposed method suppresses side effects such as detail loss or washed out appearance. Moreover, parameter calculated automatically with low computation complexity. As a result, it could applies FPD directly.