In this paper we investigate the suitability of Gabor Wavelets for an adaptive partial reconstruction of holograms based on the viewer position. Matching Pursuit is used for a sparse light rays decomposition of holographic patterns. At the decoding stage, sub-holograms are generated by selecting the diffracted rays corresponding to a specific area of visualization. The use of sub-holograms has been suggested in the literature as an alternative to full compression, by degrading a hologram with respect to the directional degrees of freedom. We present our approach in a complete framework for color digital holograms compression and explain, in details, how it can be efficiently exploited in the context of holographic Head-Mounted Displays. Among other aspects, encoding, adaptive reconstruction and selective degradation are studied.
The hybrid point-source/wave-field method is a newly proposed approach for Computer-Generated Hologram (CGH) calculation, based on the slicing of the scene into several depth layers parallel to the hologram plane. The complex wave scattered by each depth layer is then computed using either a wave-field or a point-source approach according to a threshold criterion on the number of points within the layer. Finally, the complex waves scattered by all the depth layers are summed up in order to obtain the final CGH. Although outperforming both point-source and wave-field methods without producing any visible artifact, this approach has not yet been used for animated holograms, and the possible exploitation of temporal redundancies has not been studied. In this paper, we propose a fast computation of video holograms by taking into account those redundancies. Our algorithm consists of three steps. First, intensity and depth data of the current 3D video frame are extracted and compared with those of the previous frame in order to remove temporally redundant data. Then the CGH pattern for this compressed frame is generated using the hybrid point-source/wave-field approach. The resulting CGH pattern is finally transmitted to the video output and stored in the previous frame buffer. Experimental results reveal that our proposed method is able to produce video holograms at interactive rates without producing any visible artifact.
We provide an efficient method of using Morlet wavelets for transforming a hologram and reconstructing parts of a scene based on the position of viewer by using a sparse set of Morlet transformed coefficients. We provide a design of a Morlet wavelet and explain an efficient discretization method for the application of view-dependent representation systems. Results are provided based on the numerical reconstruction, and it is shown that view- dependent representation along with Morlet wavelets form a good starting step for compressing holographic data for next generation 3DTV applications.
An analysis and discussion on the relevance of various wavelet schemes for hologram compression and reconstruction when the rendering configuration makes it possible to exploit selective refinements to perform a viewpointbased degraded reconstruction. It is observed that Gabor wavelet bases have better time-frequency localization as compared to Fresnelet bases and hence are well suited for view- dependent compression techniques for hologram reconstruction.
In this paper we propose a new method based on Second Generation Wavelets (SGW), a recently emerged mathematic transform, already successfully applied in 3d coding. This new wavelet transform (SGW) applied to meshes is performed using the powerful lifting scheme, keeping the main features of the first generation wavelets whilst introducing some useful properties such as: analysis on general domains (typically arbitrary, non linear domains of Rn), adapted basis for weighted approximation/interpolation and weighted measure and transform adapted to irregular sampled data. Thus in this paper we propose a video coder based on second generation wavelet theory and mesh geometry. In fact, we focus on video coder error images which are mainly composed of high frequencies and singularities, i.e. typically the kind of data that second generation wavelet can successfully process. The main advantage is that these wavelets can be designed to fit exactly on singularities, shapes, textures, and edges. Hence, we can further reduce redundancy and improve coding efficiency over those peculiar settings.