Web-Based Visualization of Very Large Scientific Astronomy Imagery

Visualizing and navigating through large astronomy images from a remote location with current astronomy display tools can be a frustrating experience in terms of speed and ergonomics, especially on mobile devices. In this paper, we present a high performance, versatile and robust client-server system for remote visualization and analysis of extremely large scientific images. Applications of this work include survey image quality control, interactive data query and exploration, citizen science, as well as public outreach. The proposed software is entirely open source and is designed to be generic and applicable to a variety of datasets. It provides access to floating point data at terabyte scales, with the ability to precisely adjust image settings in real-time. The proposed clients are light-weight, platform-independent web applications built on standard HTML5 web technologies and compatible with both touch and mouse-based devices. We put the system to the test and assess the performance of the system and show that a single server can comfortably handle more than a hundred simultaneous users accessing full precision 32 bit astronomy data.Comment: Published in Astronomy & Computing. IIPImage server available from http://iipimage.sourceforge.net . Visiomatic code and demos available from http://www.visiomatic.org

Graded quantization for multiple description coding of compressive measurements

Compressed sensing (CS) is an emerging paradigm for acquisition of compressed representations of a sparse signal. Its low complexity is appealing for resource-constrained scenarios like sensor networks. However, such scenarios are often coupled with unreliable communication channels and providing robust transmission of the acquired data to a receiver is an issue. Multiple description coding (MDC) effectively combats channel losses for systems without feedback, thus raising the interest in developing MDC methods explicitly designed for the CS framework, and exploiting its properties. We propose a method called Graded Quantization (CS-GQ) that leverages the democratic property of compressive measurements to effectively implement MDC, and we provide methods to optimize its performance. A novel decoding algorithm based on the alternating directions method of multipliers is derived to reconstruct signals from a limited number of received descriptions. Simulations are performed to assess the performance of CS-GQ against other methods in presence of packet losses. The proposed method is successful at providing robust coding of CS measurements and outperforms other schemes for the considered test metrics

WCAM: secured video surveillance with digital rights management

The WCAM project aims to provide an integrated system for secure delivery of video surveillance data over a wireless network, while remaining scalable and robust to transmission errors. To achieve these goals., the content is encoded in Motion-JPEG2000 and streamed with a specific RTP protocol encapsulation to prevent the loss of packets containing the most essential data. Protection of the video data is performed at content level using the standardized JPSEC syntax along with flexible encryption of quality layers or resolution levels. This selective encryption respects the JPEG2000 structure of the stream, not only ensuring end-to-end ciphered delivery, but also enabling dynamic content adaptation within the wireless network (quality of service, adaptation to the user's terminal). A DRM (Digital Rights Management) solution, called OpenSDRM is added to manage all authenticated peers on the WLAN (from end-users to cameras), as well as to manage the rights to access and display conditionally the video data. This whole integrated architecture addresses several security problems such as data encryption, integrity, access control and rights management. Using several protection lavers, the level of confidentiality can depend both on content characteristics and user rights, thus also addressing the critical issue of privacy.info:eu-repo/semantics/acceptedVersio