A one-dimensional novel-look-up-table (1-D N-LUT) has been implemented on the graphics processing unit of GTX 690 for the real-time computation of Fresnel hologram patterns of three-dimensional (3-D) objects. For that, three types of optimization techniques have been employed, which include the packing technique of input 3-D object data and the managing techniques of on-chip shared memory and registers. Experimental results show that the average hologram calculation time for one object point of the proposed system has been found to be 0.046 ms, which confirms that the proposed system can generate almost 3 frames of Fresnel holograms with 1920×1080 pixels per second for a 3-D object with 8000 object points.
KEYWORDS: Computer generated holography, Video, 3D image processing, Holograms, Image segmentation, Fringe analysis, 3D video compression, Video compression, Holography, 3D displays
Thus far, various approaches to generate the computer-generated holograms (CGHs) of 3-D objects have been suggested
but, most of them have been applied to the still images, not to the video images due to their computational complexity.
Recently, a method to fast compute the CGH patterns of 3-D video images has been proposed by combined use of data
compression and novel look-up table (N-LUT) techniques. In this method, temporally redundant data of 3-D video
images are removed with the differential pulse code modulation (DPCM) algorithm and then the CGH patterns for these
compressed video images are calculated with the N-LUT method. However, as the 3-D objects move rapidly, image
differences between the video frames may increase, which results in a massive growth of calculation time of the video
holograms. Therefore, we propose a novel approach to significantly reduce the computation time of 3-D video holograms
by employing a new concept of motion-vector of the 3-D object. In the proposed method, 3-D objects are firstly
segmented from the 1st frame of the 3-D videos, and the CGH patterns for each segmented object are computed with the
N-LUT algorithm. Secondly, motion vectors between each segmented object and the corresponding objects in the
consecutive 3-D video frames are calculated. Thirdly, the CGH patterns for each segmented object are shifted with the
calculated motion vectors. Finally, all these shifted CGH patterns are added up to generate the hologram patterns of the
consecutive 3-D video frames. To confirm the feasibility of the proposed method, experiments are performed and the
results are comparatively discussed with the conventional methods in terms of the number of object points and
computation time.
In this paper, we propose a new approach for accelerated computation of the computer-generated hologram (CGH) of a
3-D object by using the N×N-point principle fringe patterns (PFPs)-based novel look-up table (N-LUT) method. Using
the two-dimensional (2-D) run-length encoding (RLE) algorithm, redundant data of a 3-D object are extracted in image
blocks and re-grouped into an N×N-point redundancy map depending on the block size. Basing on this redundancy map,
the N×N-point PFPs are calculated, from which the CGH pattern of a 3-D object can be generated. With the block-based
extraction of the redundant data and the N×N-point PFPs-based computation of the CGH pattern, the object points to be
calculated could be significantly reduced, which results in a great increase in the computational speed. Experimental
results show that for 3×3-point PFPs, the computational speed of the proposed method has been improved by 61.16%
compared to the conventional N-LUT method.
In this paper, a new method for efficient generation of video hologram for 3-D video is proposed by combined use of
redundant data of 3-D video and look-up table techniques. That is, temporal redundant data in inter-frame of 3-D video
image is removed using differential pulse code modulation (DPCM) method between frames then inter-line redundant
data in intra-frame of 3-D video image is also removed using DPCM algorithm between lines. Experimental results show
that, in the proposed method, the average number of calculated object points for 3-D objects have been reduced by
23.3% for top-down scanning scheme, compared to the conventional TR_NLUT method. And, the average calculation
time for one object point has been also reduced by 21.0%, compared to the conventional TR_NLUT. Good experimental
results with 3-D test moving pictures finally confirmed the feasibility of the proposed method in fast generation of CGH
patterns for 3-D videos.
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