Hyo-Moon Cho

A Fast Motion Estimation Algorithm for SAD Optimization in Sub-pixel

The motion estimation is the most important technique in the image compression of the video standards. In the case of next generation standards in the video codec as H.264, a high compression-efficiency can be also obtained by using a motion compensation. To obtain the accurate motion search, a motion estimation should be achieved up to 1/2 pixel and 1/4 pixel units. To do this, the computational complexity is increased although the image compression rate is increased. Therefore, in this paper, we propose the advanced sub-pixel block matching algorithm to reduce the computational complexity by using statistical characteristics of SAD (sum of absolute difference). Generally, the probability of the minimum SAD values is high when searching point is in the distance 1 from the reference point. Thus, we reduced the searching area and then we can overcome the computational complexity problem. The main concept of proposed algorithm, which based on TSS (three step search) method, first we find three minimum SAD points which is in integer distance unit, and then, in second step, the optimal point is in 1/2 pixel unit either between the most minimum SAD value point and the second minimum SAD point or between the most minimum SAD value point and the third minimum SAD point In third step, after finding the smallest SAD value between two SAD values on 1/2 pixel unit, the final optimized point is between the most minimum SAD value and the result value of the third step, in 1/2 pixel unit i.e., 1/4 pixel unit in totally. The conventional TSS method needs an eight search points in the sub-pixel steps in 1/2 pixel unit and also an eight search points in 1/4 pixel, to detect the optimal point. However, in proposed algorithm, only total five search points are needed. In the result, 22% improvement of processing speed is obtained.

An Adaptive Method for Lane Marking Detection Based on HSI Color Model

Lane marking detection is the problem of estimating the lane boundary of a road on the image captured by a camera. This paper proposed an adaptive method based on HSI color model to detect lane marking. First, we convert RGB-based image to its HSI-based image. However, HSI color model is improved by the change in the way to calculate the intensity (I) component from RGB color images. From observing the color images of the road scene in HSI color space, we utilized the limited range of color. Hence, H, S and I component are used in this method. Just simple operations, we can detect lane marking in various road images. By comparing the results of the proposed method with other methods using RGB color model and the same method in classical HSI color model which doesn’t change the intensity component, the proposed method can label the location of lane marking accurately.

An adaptive method for detecting lane boundary in night scene

Lane detection plays a key role in the vision-based driver assistance system and is used for vehicle navigation, lateral control, collision prevention, or lane departure warning system. In this paper, we present an adaptive method for detecting lane marking based on the intensity of road images in night scene which is the cause of numerous accidents. First, a region of interest (ROI) image is extracted from the original image and converted to its grayscale image in which the value of each pixel is the maximum value of R, G and B channel of ROI image. After that, we find the maximum intensity on each row of grayscale image. Finally, the lane boundary is detected by Hough transform. Experiment results indicate that the proposed approach was robust and accurate in night scene.

A simple method for correcting lens distortion in low-cost camera using geometric invariability

In this paper, a lens distortion correction method for low-cost digital camera is proposed. The distortion coefficient and distortion center are estimated by using geometric invariants of perspective projection. The geometric invariants, including cross ratio of collinear points and straight-parallel-perpendicular lines, will be invariant in transforming from the world coordinate system to image coordinate system if there exists no distortion. We derive new distortion measure that is based on these geometric properties and can be optimized with nonlinear search technique. The method is easy to apply and lead to robust results with moderate effort. We verify accuracy and efficiency from experiments.

Performance enhancement of sum of absolute difference (SAD) computation in H.264/AVC using saturation arithmetic

Sum of Absolute Difference (SAD) Computation is commonly used for motion estimation in video coding. It is usually the computationally intensive part in video processing. Therefore, a method to reduce the computational complexity is strictly required. In this paper, the effectiveness of saturation arithmetic on SAD computation is presented. Our goal is to use saturation arithmetic to reduce the complexity of SAD computation for the encoding process while the accuracy in finding the best matching block from the reference frame is still maintained. Experiment results show that the computational complexity of SAD computation is reduced efficiently by saving a number of bits for SAD values representation while the video quality is kept.

A Novel Signal-Mapping Based LVDT Signal Conditioner

We propose a novel displacement measurement method in the LVDT (Linear Variable Differential Transformer) structure. This proposed algorithm is independent of coil pattern, which may be implemented to PCB, or transformer component, because it is based on the signal-mapping method. That is, the proposed algorithm uses not absolute measurement value but relative value. The LVDT structure consists of dual transformers or coils; in this case, it is very difficult to have exact duality for these coil pattern on a PCB or two transformer components. In practice, therefore, several tuning processes required are carried out. Whereas, proposed signal-mapping based algorithm needs just once calibration process at the final stage in manufacturing process. The proposed algorithm can easily change the sensing accuracy, operation region, output value region, and so on. And also the proposed algorithm can transform the non-linear characteristics of input signal to the linear in the calibration operation. To verify our signal-mapping based algorithm, we have manufactured several boards which have different coil patterns and our algorithm is ported into TMS320F2812 of TI DSP chipset. The output signal has high accuracy and high stability although PCB coil pattern are coarse.

Super-resolution algorithm based on discrete Fourier transform

Super-resolution (SR) processing reconstructs a high-resolution image from a set of low-resolution images for same scene. Spatial domain approaches in the super resolution algorithm were widely used. In this paper, we purposed an algorithm that converts the spatial domain into the frequency domain through the 2-dimension DFT for four low-resolution images. Utilizing the horizontal and vertical DFT (Discrete Fourier Transform) phase spectrum carry the horizontal and vertical direction feature information in frequency domain, we can make a high resolution image presented more visible details. We verify accuracy and efficiency from experimental results.

A Super Resolution Reconstruction Architecture Utilizing Registration Error Minimizing

The high-resolution image reconstruction methods utilizing a super-resolution (SR) technique better can obtain higher quality image than the conventional interpolation methods when the input images are well registered onto a common high-resolution grid. Therefore, low-resolution input images should be carefully selected to take the minimized registration error. In this paper, we propose the input image evaluation algorithm to select the suitable input image with low registration error, by using the statistical feature of the motion-compensated low-resolution images. Maximum motion compensation error (MMCE) is estimated from the high-resolution image observation model and is used to evaluate the suitability of the low-resolution input image candidates. The low-resolution input image is selected when its motion compensation error (MCE) is in the range 0 < MCE < MMCE. The reference input image (RII) is selected by counting the number of selected low-resolution input images (SLRII) from all low-resolution input images (LRII). A good high-resolution image is reconstructed efficiently from a optimal reference input image (ORII) and the selected low-resolution input images (SLRII) by using the Hardie’s SR reconstruction algorithm.

Design of advanced block matching algorithm by using RAVR

The block matching algorithm (BMA) is one of the most important processing in the video compression. Since the sub-pixel motion estimation and motion compensation are needed, the computational complexity of the BMA is increased. Recently, the sum of absolute difference (SAD) calculation is widely used for BMA but it accounted for much of the total computation of the video compression. To implement the real-time video compression, the fast algorithm for motion estimation and motion compensation based on SAD computation is needed. The partial distortion elimination (PDE) scheme is one of the most advanced methods to decrease the SAD computational complexity. The basic concept of the PDE is that if the accumulated SAD values are greater than the given accumulated SAD value then the SAD computation is stopped. Where, the given accumulated SAD value is a kind of average value. Therefore, the big problem of the PDE is that the division is needed. And, as initial accumulated SAD value is large, PDE operation becomes efficient. Thus scan order is also important in SAD computation. In this paper, we introduce the new average computation method for PDE operation without division, its mathematical modeling and architecture. The new computational method is named as RAVR (Rough Average). And we propose the advanced scan order for efficient PDE scheme based on ARVR concept. Thus, our proposed algorithm combines above two main concepts and suffers the improving SAD performance and the easy hardware implementation methods.

Necessity of Accurately Registration Parameters Calculation for Good Reconstruction in Super-Resolution from a Set of Low Images

High resolution methods have developed in the many decades. Purpose is to be obtained high resolution images. Overcome constrain of digital camera. From a set of low resolution, under-sampled, shifted, rotated and aliased images, a high resolution image is reconstructed. One of the images of the scene is a reference image. Taking advantage of aliasing-free part of the frequency domain in the images is used to calculate sub-pixel shift, angle rotate. Using the Papouis-Gerchberg algorithm to reconstruct high resolution image, which are known the relative image positions, from a set of low resolution image. The image results were archived with almost free-aliasing and good visual results.