Several approaches for increasing the speed in computation of the digital holograms of three-dimensional objects have been presented with applications to real-time display of holographic images. Among them, a look-up table (LUT) approach, in which the precalculated principal fringe patterns for all possible image points of the object are provided, has gained a large speed increase in generation of computer-generated holograms. But the greatest drawback of this method is the enormous memory size of the LUT. A novel approach to dramatically reduce the size of the conventional LUT, still keeping its advantage of fast computational speed, is proposed, which is called here a novel LUT (N-LUT) method. A three-dimensional object can be treated as a set of image planes discretely sliced in the z direction, in which each image plane having a fixed depth is approximated as some collection of self-luminous object points of light. In the proposed method, only the fringe patterns of the center points on each image plane are precalculated, called principal fringe patterns (PFPs) and stored in the LUT. Then, the fringe patterns for other object points on each image plane can be obtained by simply shifting this precalculated PFP according to the displaced values from the center to those points and adding them together. Some experimental results reveal that the computational speed and the required memory size of the proposed approach are found to be 69.5 times faster than that of the ray-tracing method and 744 times smaller than that of the conventional LUT method, respectively.

In this paper we propose a new approach for fast generation of computer-generated holograms (CGHs) of a 3D object by using the run-length encoding (RLE) and the novel look-up table (N-LUT) methods. With the RLE method, spatially redundant data of a 3D object are extracted and regrouped into the N-point redundancy map according to the number of the adjacent object points having the same 3D value. Based on this redundancy map, N-point principle fringe patterns (PFPs) are newly calculated by using the 1-point PFP of the N-LUT, and the CGH pattern for the 3D object is generated with these N-point PFPs. In this approach, object points to be involved in calculation of the CGH pattern can be dramatically reduced and, as a result, an increase of computational speed can be obtained. Some experiments with a test 3D object are carried out and the results are compared to those of the conventional methods.

Even though there are many types of methods to generate CGH (computer-generated hologram) patterns of three-dimensional (3D) objects, most of them have been applied to still images but not to video images due to their computational complexity in applications of 3D video holograms. A new method for fast computation of CGH patterns for 3D video images is proposed by combined use of data compression and lookup table techniques. Temporally redundant data of the 3D video images are removed with the differential pulse code modulation (DPCM) algorithm, and then the CGH patterns for these compressed videos are generated with the novel lookup table (N-LUT) technique. To confirm the feasibility of the proposed method, some experiments with test 3D videos are carried out, and the results are comparatively discussed with the conventional methods in terms of the number of object points and computation time.

A novel approach for fast generation of video holograms of three-dimensional (3-D) moving objects using a motion compensation-based novel-look-up-table (MC-N-LUT) method is proposed. Motion compensation has been widely employed in compression of conventional 2-D video data because of its ability to exploit high temporal correlation between successive video frames. Here, this concept of motion-compensation is firstly applied to the N-LUT based on its inherent property of shift-invariance. That is, motion vectors of 3-D moving objects are extracted between the two consecutive video frames, and with them motions of the 3-D objects at each frame are compensated. Then, through this process, 3-D object data to be calculated for its video holograms are massively reduced, which results in a dramatic increase of the computational speed of the proposed method. Experimental results with three kinds of 3-D video scenarios reveal that the average number of calculated object points and the average calculation time for one object point of the proposed method, have found to be reduced down to 86.95%, 86.53% and 34.99%, 32.30%, respectively compared to those of the conventional N-LUT and temporal redundancy-based N-LUT (TR-N-LUT) methods.

We have implemented experimental code to compute a compensated phase-added stereogram (CPAS), which was proposed in a previous paper, on a graphic processing unit (GPU). In this paper, we show an acceleration method for CPAS computation by means of the GPU and compare the computation time between CPU-based and GPU-based calculations, which are programmed in our laboratories. In addition, we demonstrate their reconstructed images. As a result, we could achieve a performance gain of a factor of over 33 compared with a CPU-based computing environment and digital holograms can be displayed at 30 frames per second with 15,000 points.

A new robust MPEG-based novel look-up table (MPEG-NLUT) is proposed for accelerated computation of video holograms of fast-moving three-dimensional (3-D) objects in space. Here, the input 3-D video frames are sequentially grouped into sets of four, in which the first and remaining three frames in each set become the reference (RF) and general frames (GFs). Then, the frame images are divided into blocks, from which motion vectors are estimated between the RF and each of the GFs, and with these estimated motion vectors, object motions in all blocks are compensated. Subsequently, only the difference images between the motion-compensated RF and each of the GFs are applied to the NLUT for CGH calculation based on its unique property of shift-invariance. Experiments with three types of test 3-D video scenarios confirm that the average number of calculated object points and the average calculation time of the proposed method, have found to be reduced down to 27.34%, 55.46%, 45.70% and 19.88%, 44.98%, 30.72%, respectively compared to those of the conventional NLUT, temporal redundancy-based NLUT (TR-NLUT) and motion compensation-based NLUT (MC-NLUT) methods.

A novel approach to extract the depth data of 3D objects in space by using the computational integral imaging reconstruction (CIIR) technique is proposed. With elemental images of 3D objects captured by the CCD camera through a pinhole array, depth-dependent object images can be reconstructed on the output plane by the CIIR technique. Only the images reconstructed on the output planes where 3D objects were located are clearly focused; so the depth data of 3D objects in space can be extracted by discriminating these focused output images from the others by using an image separation technique. A feasibility test of the proposed CIIR-based depth extraction method is carried out, and its results are discussed as well.

We propose a novel approach to massively reduce the memory of the novel look-up table (N-LUT) for computer-generated holograms by employing one-dimensional (1-D) sub-principle fringe patterns (sub-PFPs). Two-dimensional (2-D) PFPs used in the conventional N-LUT method are decomposed into a pair of 1-D sub-PFPs through a trigonometric relation. Then, these 1-D sub-PFPs are pre-calculated and stored in the proposed method, which results in a remarkable reduction of the memory of the N-LUT. Experimental results reveal that the memory capacity of the LUT, N-LUT and proposed methods have been calculated to be 149.01 TB, 2.29 GB and 1.51 MB, respectively for the 3-D object having image points of 500 × 500 × 256, which means the memory of the proposed method could be reduced by 103 × 10(6) fold and 1.55 × 10(3) fold compared to those of the conventional LUT and N-LUT methods, respectively.