Geometry-driven quantization for omnidirectional image coding

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In 1970, Furth and Yoshikawa (1970 Phys. Fluids 13 2593-6) introduced the scalings of adiabatic plasma compression. Basically, if the shape of the external plasma boundary and the aspect ratio are preserved during the compression, then the density, kinetic ...

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Reinterpretable cameras are defined by their post-processing capabilities that exceed traditional imaging. We present "SoDaCam" that provides reinterpretable cameras at the granularity of photons, from photon-cubes acquired by single-photon devices. Photon ...

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As a key unit of future high-throughput communications, optical analog to digital converter (OADC) with all-optical quantizer element that simultaneously possesses high resolution, large bandwidth and compact size is highly promising. A pending issue of co ...

Despite the increasing understanding of bentonite behaviour, there is still missing evidence on how different hydro-mechanical loadings, including sequences of hydration and compression, affect the fabric and the volume change behaviour of the material. It ...

The growing adoption of point clouds as an imaging modality has stimulated the search for efficient solutions for compression. Learning-based algorithms have been reporting increasingly better performance and are drawing the attention from the research com ...

Codebook-based optimizations are a class of algorithmic-level transformations able to effectively reduce the computing and memory requirements of Convolutional Neural Networks (CNNs). This approach tightly limits the number of unique weights in each layer, ...

State-of-the-art 2D image compression schemes rely on the power of convolutional neural networks (CNNs). Although CNNs offer promising perspectives for 2D image compression, extending such models to omnidirectional images is not straightforward. First, omn ...

A logconcave likelihood is as important to proper statistical inference as a convex cost function is important to variational optimization. Quantization is often disregarded when writing likelihood models, ignoring the limitations of the physical detectors ...

Due to the increasing number of pictures captured and stored every day by and on digital devices, lossy image compression has become inevitable to limit the needed storage requirement. As a consequence, these compression methods might introduce some visual ...

We consider distributed optimization over several devices, each sending incremental model updates to a central server. This setting is considered, for instance, in federated learning. Various schemes have been designed to compress the model updates in orde ...

Geometry-driven quantization for omnidirectional image coding

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Shafranov shift correction to the Furth-Yoshikawa scaling of tokamak adiabatic compression

Integrated electronics for time-of-flight positron emission tomography photodetectors

SoDaCam: Software-defined Cameras via Single-Photon Imaging

Increasing the Energy Efficiency of Gas Boosters for Hydrogen Storage and for Refueling Stations

Cascaded all-optical quantization employing step-size MMI and shape-optimized power splitter

Volume change response and fabric evolution of granular MX80 bentonite along different hydro-mechanical stress paths

Latent Space Slicing for Enhanced Entropy Modeling in Learning-Based Point Cloud Geometry Compression

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Convex Quantization Preserves Logconcavity

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Stochastic distributed learning with gradient quantization and double-variance reduction