Advanced methods and deep learning for video and satellite data compression

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Using CCSDS image compression standard for SAR raw data compression in the H2020 EO-ALERT project

In this paper, we describe compression strategies currently under consideration in the H2020 EO-ALERT project. In particular, we investigate the performance of the CCSDS-123.0-B Issue 2 standard for image compression when used for the purpose of compression of synthetic aperture radar (SAR) raw data onboard of satellite systems

Onboard Data Reduction for Multispectral and Hyperspectral Images via Cloud Screening

In this paper we propose a lossless and lossy onboard compression algorithm for multispectral and hyperspectral images, based on the recent CCSDS-123.0-B-2 standard, which takes advantage of cloud screening in order to perform data volume reduction, by avoiding to transmit pixels that are covered by clouds. In particular, we develop methods addressing two problems: i) how to signal the cloud mask in the compressed file, and ii) how to handle cloudy pixels in order to maximize the amount of compression. Experimental results on a set of LANDSAT 8 ETM+ and AVIRIS images show a significant data volume reduction with respect to the plain use of the CCSDS-123.0-B-2 standard

Onboard Processing Capabilities of an Earth Observation Compressive Sensing Payload

In this paper, we explore the onboard processing capabilities of an optical Earth observation instrument operating under the principles of compressed sensing, currently under preliminary study. In particular, we focus on two main aspects for onboard operations: i) how to process measurements in a computationally-efficient way to obtain previews of the reconstructed image that can be easily used by downstream inference algorithms; ii) the possibility of having simultaneous compression and encryption by proper management of the pseudorandom patterns used for the sensing matrix and measurements

Deep motion‐compensation enhancement in video compression

This work introduces the multiframe motion-compensation enhancement network (MMCE-Net), a deep-learning tool aimed at improving the performance of current video coding standards based on motion-compensation, such as H.265/HEVC. The proposed method improves the inter-prediction coding efficiency by enhancing the accuracy of the motion-compensated frame and thereby improving the rate-distortion performance. MMCE-Net is a neural network that jointly exploits the predicted coding unit and two co-located coding units from previous reference frames to improve the estimation of the temporal evolution of the scene. This letter describes the architecture of MMCE-Net, how it is integrated into H.265/HEVC and the corresponding performance

Diffraction Efficiency-Aware Reconstruction for Compressive Sensing in the Mid-Infrared

Compressive sensing has established itself as a novel imaging paradigm. In this paper, we analyze the behavior of a a compressive instrument based on spatial light modulators (SLM), operating in the mid-infrared. We show that, contrary to the well-studied visible and near-infrared wavelengths, mid-infrared poses modeling challenges due to non-negligible SLM diffraction effects. We show a way to model such effect analytically and to account for them in the reconstruction process, leading to improved reconstruction quality

Deep Learning Methods for Satellite Compressive Imaging

Session 5B-Prette-Deep Learning Methods for Satellite Compressive Imagin