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.
signal processing methods from software-driven computer engineering and applied mathematics. The compressed
sensing theory in particular established a practical framework for reconstructing the scene content using few linear
combinations of complex measurements and a sparse prior for regularizing the solution. Compressed sensing found
direct applications in digital holography for microscopy. Indeed, the wave propagation phenomenon in free space
mixes in a natural way the spatial distribution of point sources from the 3-dimensional scene. As the 3-dimensional
scene is mapped to a 2-dimensional hologram, the hologram samples form a compressed representation of the
scene as well. This overview paper discusses contributions in the field of compressed digital holography at the
micro scale. Then, an outreach on future extensions towards the real-size macro scale is discussed. Thanks to
advances in sensor technologies, increasing computing power and the recent improvements in sparse digital signal
processing, holographic modalities are on the verge of practical high-quality visualization at a macroscopic scale
where much higher resolution holograms must be acquired and processed on the computer.
Integral imaging is a technology based on plenoptic photography that captures and samples the light-field of a scene through a micro-lens array. It provides views of the scene from several angles and therefore is foreseen as a key technology for future immersive video applications. However, integral images have a large resolution and a structure based on micro-images which is challenging to encode. A compression scheme for integral images based on view extraction has previously been proposed, with average BD-rate gains of 15.7% (up to 31.3%) reported over HEVC when using one single extracted view. As the eﬃciency of the scheme depends on a tradeoﬀ between the bitrate required to encode the view and the quality of the image reconstructed from the view, it is proposed to increase the number of extracted views. Several configurations are tested with diﬀerent positions and diﬀerent number of extracted views. Compression eﬃciency is increased with average BD-rate gains of 22.2% (up to 31.1%) reported over the HEVC anchor, with a realistic runtime increase.
Side Information (SI) has a strong impact on the rate-distortion performance in distributed video coding.
The quality of the SI can be impaired when the temporal distance between the neighboring reference frames
increases. In this paper, we introduce two novel methods that allow improving the quality of the SI. In the
first approach, we propose a new estimation method for the initial SI using backward and forward motion
estimation. The second one consists in re-estimating the SI after decoding all WZFs within the current
Group of Pictures (GOP). For this purpose, the SI is first successively refined after each decoded DCT band.
Then, after decoding all WZFs within the GOP, we adapt the search area to the motion content. Finally,
each already decoded WZF is used, along with the neighboring ones, to estimate a new SI closer to the
original WZF. This new SI is then used to reconstruct again the WZF with better quality. The experimental
results show that, compared to the DISCOVER codec, the proposed method reaches an improvement of up
to 3.53 dB in rate-distortion performance (measured with the Bjontegaard metric) for a GOP size of 8.
Differential motion estimation produces dense motion vector fields which are far too demanding in terms of
coding rate in order to be used in video coding. However, a pel-recursive technique like that introduced by
Cafforio and Rocca can be modified in order to work using only the information available at the decoder side.
This allows to improve the motion vectors produced in the classical predictive modes of H.264.
In this paper we describe the modification needed in order to introduce a differential motion estimation
method into the H.264 codec. Experimental results will validate a coding mode, opening new perspectives in
using differential-based motion estimation techniques into classical hybrid codecs.
The most recent video coding standard H.264 achieves excellent compression performances at many different
bit-rates. However, it has been noted that, at very high compression ratios, a large part of the available coding
resources is only used to code motion vectors. This can lead to a suboptimal coding performance. This paper
introduces a new coding mode for a H.264-based video coder, using quantized motion vector (QMV) to improve
the management of the resource allocation between motion information and transform coeffcients. Several
problems have to be faced with in order to get an efficient implementation of QMV techniques, yet encouraging
results are reported in preliminary tests, allowing to improve the performances of H.264 at low bit-rates over