Juno perijove 6, May 19, 2017 |
Juno perijove 6, May 19, 2017 |
May 4 2017, 05:57 PM
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Senior Member Group: Members Posts: 2346 Joined: 7-December 12 Member No.: 6780 |
Voting for Perijove 06 started yesterday, and it will last for another almost 7 days.
This time, I'm not quite free of bias, since I'm interested in an extension of the polar time-lapse sequence, especially in a coverage of the north and south polar FFRs and the presumed edge of the respective polar haze disks. I think - well, I'm rather certain - that it's possible to infer short-time dynamics of the FFRs, and of the vortices near the edge of the haze disk. Due to the expected good contact to Earth during the PJ-6 pass we have a good chance to obtain overlapping images of these regions. More in the discussion section on the missionjuno site. Of course, there are other interesting targets, too; see Glenn's and John Rogers' (Philosophia-47) comments. A full latitude coverage would allow for a pole-to-pole animation. |
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Jun 22 2017, 11:49 PM
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IMG to PNG GOD Group: Moderator Posts: 2254 Joined: 19-February 04 From: Near fire and ice Member No.: 38 |
I have been running into an interesting issue when processing some of the images. Here is an example, an enhanced crop from image PJ06_119:
The arrows point in the direction of a color discontinuity. The color change at left is because of a drop in the green intensity to the right of the discontinuity and the color change at right (which is more subtle) is because of a drop in the red intensity. These color changes are not a real feature (I verified this by checking image PJ06_118 which also shows this area). As a sanity check I checked Gerald's images and this also appears there. It occurred to me that this might be a consequence of how the images are decompanded. In the original, raw image the intensity near the color change is ~210 in both cases (green and red). Keeping this in mind the companding table is rather interesting. Below are a few lines from the table. The columns are 8-bit_SQROOT_Value, 12-bit_Linear_Value and the change from the previous value in the 12 bit column. 207 1375 16 208 1391 16 209 1407 16 <--- 210 1439 32 <--- 211 1471 32 212 1503 32 Note that the difference between adjacent values jumps from 16 to 32. Of course this is normal but I strongly suspect that this creates visual artifacts (and maybe the human visual system also exaggerates the difference because of a Mach band effect or something similar). I plan on exploring this further to see if I'm right (I'm not yet completely sure). I may even experiment with a slightly modified companding table. Strictly speaking this would result in slightly less accurate images where the intensity is ~210 but aesthetically they would look better if I'm successful. I'm interesting in knowing if someone else (Gerald?) has also explored this issue. |
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Jun 23 2017, 04:59 PM
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Senior Member Group: Members Posts: 2542 Joined: 13-September 05 Member No.: 497 |
I'm interesting in knowing if someone else has also explored this issue. Since the companding maps all input values from 1392 to 1407 to 209 (for example) there's no way to uniquely recover the input, obviously. This is more obvious for Junocam where the companding is done in a piecewise linear fashion and there is a slope change at 209-210 (in contrast to MSL where a smoother full table is used). At various times people have talked about intentionally adding gaussian noise back into the decompanded values, but I've never tried this. Depending on where in the processing flow you do the decompanding relative to other operations (photometric removal, for example) and what precision you are doing this to, you may or may not be quantizing the values further. -------------------- Disclaimer: This post is based on public information only. Any opinions are my own.
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Nov 2 2017, 12:53 PM
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Senior Member Group: Members Posts: 2346 Joined: 7-December 12 Member No.: 6780 |
... At various times people have talked about intentionally adding gaussian noise back into the decompanded values... Here is a test run comparing discrete decompanding with randomized decompanding. The randomized decompanding mimics the loss of information during companding by using any of the possible raw data numbers resulting in the companded value with equally distributed probability (discrete uniform distribution). To my eyes, there is some low-frequency smoothing effect (plus subtle high-frequency noise), but insufficient to fully adjust for the color discontinuity. |
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