• Journal of Broadcast Engineering
• /
• v.14 no.5
• /
• pp.551-560
• /
• 2009

This paper proposes a method for efficient lossless depth map coding for MPEG 3D-Video coding. In general, the conventional video coding method such as H.264 has been used for depth map coding. However, the conventional video coding methods do not consider the image characteristics of the depth map. Therefore, as a lossless depth map coding method, this paper proposes a bit-plane based lossless depth mar coding method by using the MPEG-4 Part 2 shape coding scheme. Simulation results show that the proposed method achieves the compression ratios of 28.91:1. In intra-only coding, proposed method reduces the bitrate by 24.84% in comparison with the JPEG-LS scheme, by 39.35% in comparison with the JPEG-2000 scheme, by 30.30% in comparison with the H.264(CAVLC mode) scheme, and by 16.65% in comparison with the H.264(CABAC mode) scheme. In addition, in intra and inter coding the proposed method reduces the bitrate by 36.22% in comparison with the H.264(CAVLC mode) scheme, and by 23.71% in comparison with the 0.264(CABAC mode) scheme.

• Journal of Multimedia Information System
• /
• v.5 no.3
• /
• pp.189-194
• /
• 2018

In this paper, we propose the drowsiness detection method for car driver. This paper determines whether or not the driver's eyes are closed using the depth and infrared videos. The proposed method has the advantage to detect drowsiness without being affected by illumination. The proposed method detects a face in the depth picture by using the fact that the nose is closest to the camera. The driver's eyes are detected by using the extraction of harr-like feature within the detected face region. This method considers to be drowsiness if eyes are closed for a certain period of time. Simulation results show the drowsiness detection performance for the proposed method.

• Journal of Multimedia Information System
• /
• v.4 no.4
• /
• pp.289-294
• /
• 2017

In this paper, we implement to make the character content with the floating hologram. The floating hologram is the one of hologram techniques for projecting the 2D image to represent the 3D image in the air using the glass panel. The floating hologram technique is easy to apply and is used in exhibitions, corporate events, and advertising events. This paper uses both the depth information and the unreal engine for the floating hologram. Simulation results show that this method can make the character content to follow the movement of the user in the real time by capturing the depth video.

• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.15 no.4
• /
• pp.943-950
• /
• 2011

In this paper, we presented an attention measurement method in 2D and 3D image/video to be applied for image and video retargeting and compressive processing. 2D attention is derived from the three main components, intensity, color, and orientation, while depth information is added for 3D attention. A rarity-based attention method is presented to obtain more interested region or objects. Displaced depth information is matched to attention probability in distorted stereo images and finally a stereo distortion predictor is designed by integrating low-level HVS responses. As results, more efficient attention scheme is developed from the conventional methods and performance is proved by applying for video retargeting.

• The KIPS Transactions:PartB
• /
• v.12B no.3 s.99
• /
• pp.247-256
• /
• 2005

One of the classic research problems in computer vision is that of stereo, i.e., the reconstruction of three dimensional shape from two or more images. This paper deals with the problem of extracting depth information of non-rigid dynamic 3D scenes from general 2D video sequences taken by monocular camera, such as movies, documentaries, and dramas. Depth of the blocks are extracted from the resultant block motions throughout following two steps: (i) calculation of global parameters concerned with camera translations and focal length using the locations of blocks and their motions, (ii) calculation of each block depth relative to average image depth using the global parameters and the location of the block and its motion, Both singular and non-singular cases are experimented with various video sequences. The resultant relative depths and ego-motion object shapes are virtually identical to human vision.

• The Journal of Korean Institute of Next Generation Computing
• /
• v.14 no.6
• /
• pp.87-97
• /
• 2018

Rainfall depth is an important meteorological information. Generally, high spatial resolution rainfall data such as road-level rainfall data are more beneficial. However, it is expensive to set up sufficient Automatic Weather Systems to get the road-level rainfall data. In this paper, we proposed to use deep learning to recognize rainfall depth from road surveillance videos. To achieve this goal, we collected two new video datasets, and proposed a new deep learning architecture named Temporal and Spatial Segment Networks (TSSN) for rainfall depth recognition. Under TSSN, the experimental results show that the combination of the video frame and the differential frame is a superior solution for the rainfall depth recognition. Also, the proposed TSSN architecture outperforms other architectures implemented in this paper.

• Smart Media Journal
• /
• v.2 no.2
• /
• pp.28-32
• /
• 2013

In this paper, we present a method to reduce complexity of depth modeling modes (DMM) that are used in the current 3D-HEVC standardization. DMM adds four modes to the existing HEVC intra prediction modes for accurate object edge representation in the depth map. Especially, Mode 3 requires distortion calculation of numerous pre-defined wedgelets, inducing high complexity. The proposed method employs absolute differences of neighboring pixels in the sides of the reference block to find high intensity changing positions. Based on such positions, the number of wedgelet candidates is reduced to six and distortion calculation is skipped for irrelevant wedgelets. Experimental results show complexity reduction by 3.1% on average, while maintaining similar coding performance.

• The Journal of the Institute of Internet, Broadcasting and Communication
• /
• v.9 no.1
• /
• pp.53-59
• /
• 2009

Multi-view video is necessary to develop a new compression encoding technique for storage and transmission, because of a huge amount of data. Layered depth image is an efficient representation method of multi-view video data. This method makes a data structure that is synthesis of multi-view color and depth image. This paper proposed enhanced compression method by presentation of efficient layered depth image using real distance comparison, solution of overlap problem, and interpolation. In experimental results, confirmed high compression performance.

• Journal of Korea Society of Industrial Information Systems
• /
• v.21 no.5
• /
• pp.11-17
• /
• 2016

In this paper, we propose a method of correcting depth image by the plane modeling and then improving the coding performance. We model a plane by using the least squares method to the horizontal and vertical directions including the target pixel, and then determine that the predicted plane is suitable from the estimate error. After that, we correct the target pixel by the plane mode. The proposed method can correct not only the depth image composed the plane but also the complex depth image. From the simulation result that measures the entropy power, which can estimate the coding performance, we can see that the coding performance by the proposed method is improved up to 80.2%.

• Proceedings of the Korean Society of Broadcast Engineers Conference
• /
• 1997.06a
• /
• pp.131-136
• /
• 1997

We present a method to estimate a dense and sharp depth map using multiple cameras for the application to flexible video production. A key issue for obtaining sharp depth map is how to overcome the harmful influence of occlusion. Thus, we first propose to selectively use the depth information from multiple cameras. With a simple sort and discard technique, we resolve the occlusion problem considerably at a slight sacrifice of noise tolerance. However, boundary overreach of more textured area to less textured area at object boundaries still remains to be solved. We observed that the amount of boundary overreach is less than half the size of the matching window and, unlike usual stereo matching, the boundary overreach with the proposed occlusion-overcoming method shows very abrupt transition. Based on these observations, we propose a hierarchical estimation scheme that attempts to reduce boundary overreach such that edges of the depth map coincide with object boundaries on the one hand, and to reduce noisy estimates due to insufficient size of matching window on the other hand. We show the hierarchical method can produce a sharp depth map for a variety of images.