Holographic three-dimensional display is a true three-dimensional display technology, has been widely used in many fields. But the big data size is a bottleneck of the application of this technology. Compressing the data of hologram is essential and beneficial to the application of holography, such as hologram transmission, holographic display. In this paper, three methods of using wavelet transform for the compression of hologram are considered: 1.using wavelet transform to compress the spatial domain information of hologram; 2. using wavelet transform to compress the amplitude and phase information in frequency domain of hologram; 3.using wavelet transform to compressing the real and imaginary information in frequency domain of hologram. Quantization and entropy coding are used for the wavelet coefficients obtained by the three methods. Numerical experiments on the compression of hologram are performed. The comparative analysis of the three methods of the compression results is performed. The final results of the experimental show that the maximum compression ratios of the three methods are 220.35, 395.17 and 365.38 respectively when the quantization level is 4. The research can give a useful reference in the application of holography.
In order to solve the problem of slow computation of point source model, we designed a real-time computer holographic generation system based on a multi-core CPUs and graphics processing unit (GPU). This system makes full use of the GPU's powerful parallel computing capabilities and CPU logic computing capabilities. It has been verified through experiments that the system is effective and feasible. At the same time, we use the Compute Unified Device Architecture (CUDA) platform to program an algorithm for the parallel computation of holograms in a graphics processing unit. In this paper, we have implemented a point source model to generate compute-generated holograms. We also compared computational performance in CPUs, GPUs, multi-core CPUs and GPUs. Among them, the multi-core CPU and GPU systems have the fastest computational holograms, which can at least increase the hologram calculation speed by 120 times compared with the equivalent CPU system, and also can increase the speed of calculation by 2 to 10 times compared with the GPU system. Therefore, the system which we designed provides a new method for real-time calculation of holograms.