Progressive hologram transmission using a view-dependent scalable compression scheme

Abstract

Over the last few years, holography has been emerging as an alternative to stereoscopic imaging since it provides users with the most realistic and comfortable three-dimensional (3D) experience. However, high quality holograms enabling a free-viewpoint visualization contain tremendous amount of data. Therefore, a user willing to access to a remote hologram repository would face high downloading time, even with high speed networks. To reduce transmission time, a joint viewpointquality scalable compression scheme is proposed. At the encoder side, the hologram is first decomposed into a sparse set of diffracted light rays using Matching Pursuit over a Gabor atoms dictionary. Then, the atoms corresponding to a given user's viewpoint are selected to form a sub-hologram. Finally, the pruned atoms are sorted and encoded according to their importance for the reconstructed view. The proposed approach allows a progressive decoding of the sub-hologram from the first received atom. Streaming simulations for a moving user reveal that our approach outperforms conventional scalable codecs such as scalable H.265 and enables a practical streaming with a better quality of experience.

Phase space formulation of light propagation on tilted planes

“…The link between a symplectomorphism 𝜒 and the kernel operator is made through a characteristic function 𝜙 that can be found by studying the submanifold Γ of R 4 × R 4 defined by {((𝑥, 𝜉), (𝑦, 𝜂)) ∈ R 4 × R 4 : (𝑦, 𝜂) = 𝜒(𝑥, 𝜉)}, which is called canonical relation [8]. A natural symplectic form Ω can be chosen on R 4 × R 4 so that Γ is a Lagrangian manifold, i.e. Ω vanishes on Γ.…”

“…However, for many applications, namely related to hologram generation, compression and adaptive display, it is required to work in the so-called phase space, i.e. the space-frequency domain [4,5]. Indeed, the representations of holograms in the space or frequency domains are highly chaotic, and a semantically meaningful regularity can only be exhibited through phase space transformations such as wavelets, Windowed Fourier Transform, Gabor frames or Wigner-Ville distribution [6].…”

Providing a Visual Understanding of Holography Through Phase Space Representations

“…[15] issued another investigation into phase space methods for DH as well as light field and integral imaging. Other authors make use of the phase space methods in order to: perform 3D triangulation of sparsely scattering scenes from 1D DHs [15,16]; devise view-dependent [17,18] or general compression schemes [19] for DH; speed up the generation of holograms [20]; derive generalized sampling theorems [21]; better understand optical operations in DH [22,23]; understand the optical bandwidth of a system [24]. In [25,26], different PSRs were studied for DH microscopy of extremely sparse scenes.…”

“…PSRs can provide interesting additional insights on the visible contents. Taking for example the defocused hologram of Figure 9 and retaining the center right via Equation (18), discards any information outside the aperture as shown in Figure 12a. As we can see all remaining information will be located near f ξ ≈ −1 in this case.…”

Compression and reconstruction of extremely-high resolution holograms based on hologram-lightfield transforms

“…In this paper, El Rhammad et al used an over-complete Gabor wavelets dictionary and Matching Pursuit algorithm to decompose the hologram into a sparse set of light beams and compressed them using an entropy coder. This method was extended to provide viewpoint scalability in [23] and progressive transmission in [24].…”