Autoregressive modeling for lossless compression of holograms.

The large number of pixels to be processed and stored for digital holographic techniques necessitates the development of effective lossless compression techniques. Use cases for such techniques are archiving holograms, especially sensitive biomedical data, and improving the data transmission capacity of bandwidth-limited data transport channels where quality loss cannot be tolerated, like display interfaces. Only a few lossless compression techniques exist for holography, and the search for an efficient technique well suited for processing the large amounts of pixels typically encountered is ongoing. We demonstrate the suitability of autoregressive modeling for compressing signals with limited spatial bandwidth content, like holographic images. The applicability of such schemes for any such bandlimited signal is motivated by a mathematical insight that is novel to our knowledge. The devised compression scheme is lossless and enables decoding architecture that essentially has only two steps. It is also highly scalable, with smaller model sizes providing an effective, low-complexity mechanism to transmit holographic data, while larger models obtain significantly higher compression ratios when compared to state-of-the-art lossless image compression solutions, for a wide selection of both computer-generated and optically-acquired holograms. We also provide a detailed analysis of the various methods that can be used for determining the autoregressive model in the context of compression.

Binary hologram compression using context based Bayesian tree models with adaptive spatial segmentation.

With holographic displays requiring giga- or terapixel resolutions, data compression is of utmost importance in making holography a viable technique in the near future. In addition, since the first-generation of holographic displays is expected to require binary holograms, associated compression algorithms are expected to be able to handle this binary format. In this work, the suitability of a context based Bayesian tree model is proposed as an extension to adaptive binary arithmetic coding to facilitate the efficient lossless compression of binary holograms. In addition, we propose a quadtree-based adaptive spatial segmentation strategy, as the scale dependent, quasi-stationary behavior of a hologram limits the applicability of the advocated modelling approach straightforwardly on the full hologram. On average, the proposed compression strategy produces files that are around 12% smaller than JBIG2, the reference binary image codec.

JPEG Pleno holography: scope and technology validation procedures.

JPEG Pleno is a standardization framework addressing the compression and signaling of plenoptic modalities. While the standardization of solutions to handle light field content is currently reaching its final stage, the Joint Photographic Experts Group (JPEG) committee is now preparing for the standardization of solutions targeting point cloud and holographic modalities. This paper addresses the challenges related to the standardization of compression technologies for holographic content and associated test methodologies.

Exact global motion compensation for holographic video compression.

Holographic video requires impractical bitrates for storage and transmission without data compression. We introduce an end-to-end compression pipeline for compressing holographic sequences with known ground truth motion. The compression strategy employs a motion compensation algorithm based on the rotational transformation of an angular spectrum. Residuals arising from the compensation step are represented using short-time Fourier transforms and quantized with uniform mid-rise quantizers whose bit depth is determined by a Lagrangian rate-distortion optimization criterion where the distortion metric is the mean squared error. Experiments use computer-generated holographic videos, and we report Bjøntegaard delta peak signal-to-noise ratio gains of around 20 dB when compared to traditional image/video codecs.