• The Journal of Korean Institute of Communications and Information Sciences
• /
• v.29 no.9C
• /
• pp.1330-1336
• /
• 2004

This paper addresses an adaptive motion vector prediction algorithm to improve the performance of video encoder. The block-based motion vector is characterized by non-stationary local statistics so that the coefficients of LS (Least Squares) based linear motion can be optimized. However, it requires very expensive computational cost. The proposed algorithm using LS approach with spatially varying motion-directed property adaptively controls the coefficients of the motion predictor and reduces the computational cost as well as the motion prediction error. Experimental results show the capability of the proposed algorithm.

• Journal of Broadcast Engineering
• /
• v.16 no.5
• /
• pp.873-882
• /
• 2011

In this paper, we propose an efficient deinterlacing algorithm which interpolates the missing scan lines by weighted summing of the intra and the inter interpolation pixels according to the spatio-temporal variation. In the spatial interpolation, we adopt a new edge based spatial interpolation method which includes edge directional refinement. The conventional edge dependent interpolation algorithms are very sensitive to noise due to the failure of estimating edge direction. In order to exactly detect edge direction, our method first finds the edge directions around the pixel to be interpolated and then refines edge direction of the pixel using weighted maximun frequent filter. Futhermore, we improve the accuracy of motion detection by reducing the possibility of motion detection error using 3 tab median filter. In the final interpolation step, we adopt weighted sum of intra and inter interpolation pixels according to spatio-temporal variation ratio, thereby improving the quality in slow moving area. Simulation results show the efficacy of the proposed method with significant improvement over the previous methods in terms of the objective PSNR quality as well as the subjective image quality.

• Journal of Korea Multimedia Society
• /
• v.13 no.2
• /
• pp.185-195
• /
• 2010

In this paper, we propose an efficient inter and intra prediction algorithms for fast encoding of H.264/AVC. First, inter prediction mode decision method decides early using temporal/spatial correlation information and pixel direction information. Second, intra prediction mode decision method selects block size judging smoothness degree with inner/outer pixel value variation and decides prediction mode using representative pixel and reference pixel. Lastly, adaptive motion search region restriction sets search region using mode information of neighboring block and predicted motion vector. The experimental results show that proposed method can achieve about 18~53% reduction compared with the existing JM 14.1 in the encoding time. In RD performance, the proposed method does not cause significant PSNR value losses while increasing bitrates slightly.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
• /
• v.2 no.3
• /
• pp.53-60
• /
• 2009

The vision-based driver fatigue detection is one of the most prospective commercial applications of facial expression recognition technology. The facial feature tracking is the primary technique issue in it. Current facial tracking technology faces three challenges: (1) detection failure of some or all of features due to a variety of lighting conditions and head motions; (2) multiple and non-rigid object tracking; and (3) features occlusion when the head is in oblique angles. In this paper, we propose a new active approach. First, the active IR sensor is used to robustly detect pupils under variable lighting conditions. The detected pupils are then used to predict the head motion. Furthermore, face movement is assumed to be locally smooth so that a facial feature can be tracked with a Kalman filter. The simultaneous use of the pupil constraint and the Kalman filtering greatly increases the prediction accuracy for each feature position. Feature detection is accomplished in the Gabor space with respect to the vicinity of predicted location. Local graphs consisting of identified features are extracted and used to capture the spatial relationship among detected features. Finally, a graph-based reliability propagation is proposed to tackle the occlusion problem and verify the tracking results. The experimental results show validity of our active approach to real-life facial tracking under variable lighting conditions, head orientations, and facial expressions.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
• /
• 2004.05b
• /
• pp.603-607
• /
• 2004

The vision-based driver fatigue detection is one of the most prospective commercial applications of facial expression recognition technology. The facial feature tracking is the primary technique issue in it. Current facial tracking technology faces three challenges: (1) detection failure of some or all of features due to a variety of lighting conditions and head motions; (2) multiple and non-rigid object tracking and (3) features occlusion when the head is in oblique angles. In this paper, we propose a new active approach. First, the active IR sensor is used to robustly detect pupils under variable lighting conditions. The detected pupils are then used to predict the head motion. Furthermore, face movement is assumed to be locally smooth so that a facial feature can be tracked with a Kalman filter. The simultaneous use of the pupil constraint and the Kalman filtering greatly increases the prediction accuracy for each feature position. Feature detection is accomplished in the Gabor space with respect to the vicinity of predicted location. Local graphs consisting of identified features are extracted and used to capture the spatial relationship among detected features. Finally, a graph-based reliability propagation is proposed to tackle the occlusion problem and verify the tracking results. The experimental results show validity of our active approach to real-life facial tracking under variable lighting conditions, head orientations, and facial expressions.