Īpply hillshading simulation with azimuth and altitude angles a, b. Return a tile with index n at resolution level r, in JPEG format. Rotate the image by r (90, 180 or 270 degrees). ĭefine a region of interest starting at relative coordinates x, y with width w and height h.
Height h in pixels of the full sized JPEG image returned by the CVT command (interpolated from the nearest resolution). Width w in pixels of the full sized JPEG image returned by the CVT command (interpolated from the nearest resolution). Return the full image or a region, in JPEG format. Specify a particular image within a set of sequences or set of multi-band images. JPEG quality factor q between 0 (worst) and 100 (best). Property/ies t e x t to be retrieved from image and server metadata. For a complete description of the protocol, see the full IIP protocol specification (Hewlett Packard, Live Picture, Eastman Kodak, 1997). Table 1 lists the main commands already available in the original, cultural heritage-oriented version of IIPimage.
Figure 1: Spectral visualization of Renoir’s Femme Nue dans un Paysage, Musée de l’Orangerie, showing spectral reflectance curve for any location and controls for comparing different imaging modalities Figure 2: Hyperspectral imaging of paintings 2) into museum research databases, providing for unprecedented levels of interactivity and access to these resources (Lahanier et al., 2002). The client-server solution also enabled integration of full resolution scientific imaging such as infra-red reflectography, Xray, multispectral and hyperspectral imagery (Fig. It had hitherto been very difficult to simply even view such image data locally, let alone access it remotely, share or collaborate between institutions. The original system was designed to be capable of handling gigapixel size, scientific-grade imaging of up to 16 bits per channel, colorimetric images encoded in the CIEL*a*b* color space and high resolution multispectral images (Martinez et al., 2002) (Fig. IIPImage has a long history and finds its roots in the mid 1990s in the cultural heritage field where it was originally created to enable the visualization of high resolution colorimetric images of paintings (Martinez et al., 1998). Hence, currently, server-side compression and encoding of the original data to a browser-friendly format remains necessary in order to achieve a satisfactory user experience on the web client, especially with high resolution screens. Moreover, lossless compression of scientific images is generally not very efficient, especially for noisy floating-point data (e.g. In practice this is currently limited to small rasters, as managing millions of such pixels in JavaScript is still too burdensome for less powerful devices. One possibility is to convert the original science data within the browser using JavaScript, either directly from FITS (Lowe, 2011 Kapadia, 2013), or from a more “browser-friendly” format, such as e.g., a special PNG “representation file” (Mandel, 2014), or compressed JSON (Federl et al., 2011). One of the difficulties in having the browser deal with science data is that browser engines are designed to display gamma-encoded images in the GIF, JPEG or PNG format, with 8-bits per Red/Green/Blue component, whereas scientific images typically require linearly quantized 16-bit or floating point values.
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. 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. It provides access to floating point data at terabyte scales, with the ability to precisely adjust image settings in real-time.
The proposed software is entirely open source and is designed to be generic and applicable to a variety of datasets. Applications of this work include survey image quality control, interactive data query and exploration, citizen science, as well as public outreach. In this paper, we present a high performance, versatile and robust client-server system for remote visualization and analysis of extremely large scientific images. 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.