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Gerald Eichstädt | PJ55 Jupiter approach reconstructed from JunoCam data with parts in 450-fold and 30-fold time-lapse @geraldeichstaedt | Uploaded 11 months ago | Updated 6 minutes ago
This video covers the Jupiter approach portion of Juno's Perijove 55 flyby, when NASA's Juno spacecraft approached Jupiter's northern circumpolar region after the Io flyby.
The first part displays 132.5 real-time minutes starting at 2023-10-15T07:45 (including fade-in) in 450-fold time-lapse and with a field of view of 80°x45°.
After a fast zoom-in over about 15 rendered still images to a field of view of 32°x18°, the video continues with a 30-fold time-lapse. The video ends near 2023-10-15T10:40, just before the spacecraft crossed Jupiter's terminator to its night side.

The video is based on 15 Perijove 55 JunoCam RGB images (product ids JNCE_2023288_55C00041_V01 to JNCE_2023288_55C00062_V01, skipping a few). The stills of the first portion of the video are rendered in time-steps of 15 real-time seconds. The stills of the second portion of the video are rendered in time-steps of one real-time second.
Both parts are converted into a video file with a rate of 30 frames per second.

For calibration purposes, after decompanding, assembling and patching the raws from several camera artefacts, linear radiometric factors (0.734; 1.0; 3.023) were applied to the (R;G;B) channels. Those weights were derived from the PJ54 RGB sequence.
The Jupiter images have mostly been illumination adjusted and gamma-stretched to the 4th power of square-root encoding in order to enhance colors and contrast.

For each of the included raw images, the stills used for their respective video scene were rendered based on Juno's SPICE trajectory data. Those scenes were blended into a continuous sequence of stills, and finally converted into an MP4 video file using the ffmpeg utility.
The stills were processed from the raws using home-made proprietary software.

Credit: NASA / JPL / SwRI / MSSS / SPICE / Gerald Eichstädt
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PJ55 Jupiter approach reconstructed from JunoCam data with parts in 450-fold and 30-fold time-lapse @geraldeichstaedt

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