EGU 2017

As a result of a travel grant awarded to me by the Remote Sensing and Photogrammetry Society, I was lucky enough to be able to return to EGU this year, albeit only for the Wednesday. I was there to present my research, in a poster format, based on raw image processing in structure-from-motion workflows. After arriving in Vienna on Tuesday afternoon I went straight the hostel I was staying at to review my poster and to finalize the sessions I would go to.

I got to the conference early in the morning, and set up my poster which was to be presented during the high resolution topography in the geosciences session. After taking a short break to grab a coffee, I headed over to the first session of the day – Imaging, measurements and modelling of physical and biological processes in soils. After last year’s fascinating run of discussions about soil and soil erosion, I decided my one day at EGU would be largely dedicated to that theme!

One particular talk which caught my eye used data fusion of laser scanning and NIR spectrometry with the goal to couple the two datasets for use in examining feedbacks in soil processes. Some very cool kit, and very blue-sky research, a good way to start the day!

After lunch, I almost exclusively attended a land degradation session, which featured some very interesting speakers. Many focused on integrating modern techniques for prevention of soil erosion and gully formation into farming practices in Africa. Interestingly, while the talks almost all focused on case studies and success in showing the physical effects of taking these actions, the Q & As were very much about social aspects, and how to bring about the cultural change within farming communities.

Another notable talk was given by a group who were aiming to promote the use of a targeted carbon economy which sees citizens from carbon consuming countries pay for the upkeep and management of forestry in developing communities. The presentation was very clear and set solid numbers onto each factor introduced, which meant it was much easier to share the vision portrayed, definitely something I’ll be following in the future!

This lead to the poster session in which I was participating, which was well attended and seemed to generate lots of interest. By the time I arrived to present at the evening session, the 15 A4 posters I had printed had been hoovered up, which is always a good sign! Over the course of the hour and a half I was visited by many people who I had met before at various conferences – it’s always nice to have people you know come to say hello, especially as affable a bunch as geomorphologists!


The poster I presented

One group of particular interest were from Trinity College Dublin, where I had done my undergraduate degree many moons ago. Niamh Cullen is doing research into coastal processes in the West of Ireland and is using photogrammetry to make some measurements, and so we had a very good discussion on project requirements/best strategy. She’s also involved in the Irish Geomorphology group, who’s remit seeks to establish a community of geomorphologists in Ireland.

In the evening I attended the ECR geomorphologist dinner, which was great fun, a good way to wrap up proceedings! I look forward to participating in EGU in the future in whatever capacity I can.

WhatsApp Images

One thing I’ve noticed since sharing images across a range of formats/websites, is that image compression algorithms on various platforms vary noticeably. This is most evident, from my experience, with WhatsApp, where images tend to be resized without even an anti-aliasing filter. The results are images with huge amounts of speckle in them when they are not resized before uploading.

Obviously the target market for WhatsApp and its user base isn’t people using high end cameras to share their images on the application, but it still seems like a couple of functions could fix a lot of the visual problems that I see, which would save me having to do it locally.

It seems astounding to me that such a big company wouldn’t put more time into sensible image compression/resizing, or perhaps they have and I am catching exceptions. The blocky artifacts I’ve written about being associated with the algorithm on this blog before are evident. Even with the third example included, where the image was resized to 20% of it’s sized before compression applied produces a much better result qualitatively, even with the smaller pixel count upon redownload of the latter.

Whilst whatever algorithm they are using is likely directed towards smartphone camera users it still seems like an oversight by the developers. Hopefully WordPress doesn’t apply a similar type of compression when I post this now!

Too much JPEG!

Having read lots about the JPEG algorithm of late in my investigations of image quality, and having written about it’s effects on image gradients in my last post, I though it would be good to include an entry about it in this blog.

Whilst I invite the more curious reader to delve into the nuances of the algorithm, which in closely related to the Fourier transform which I’ve written about previously, today I’ll be looking past the black box by testing the same key parameter as in the last post which the user has control over, the ‘quality‘ setting. One thing we will note, however, is that the JPEG algorithm operates on 8 x 8 discrete pixel windows, which is one of the more noticeable things when the algorithm is applied at lower quality settings.

Let’s have a look at the impact of varying the quality of a cropped portion (1000 x 1000 pixels) of an image:

The impact at the lower end of the JPG images is dramatic. As the quality is set to 1, 8 x 8 pixel blocks are essentially assigned the same value, and so the image will downgrade visibly. As we increase the quality parameter, this compression will start to disappear, but at quality 25 we can still see some degree of ‘blockiness’ due to the 8 x 8 pixel windows still varying to a large enough degree.

However, past around quality 50 the impact is much more subtle, and I tend not to be able to tell the difference for images cropped to this size. This elucidates the point: The JPG algorithm is amazing at the amount one can save, in terms of file size, in an image.

Let’s take a look at one more set of crops, this time the same image as above, but cropped to just 200 x 200 pixels:

The ‘blockiness’ is certainly evident at quality 50, and less subtle but notable at quality 75. I think the most astounding thing is the lack of perceptible difference between quality 92 and 100, given the file size difference. We can investigate where the difference lies using a comparison image (imagemagick’s compare function), where red pixels show different values. I will also include the difference image between the two cropped sections, which should offer some insight into the spatial distribution of pixel variations, if any exist:

So The mean variation between digital numbers for pixels in each 8 bit band is 1.5, but the file size saving is nearly 75%! The difference image shows that the digital number differences are concentrated in areas of high frequency information, such as along the cracks in the rock wall, areas which could be very important in delineating boundaries, for example.

While subtle, for work which involves photogrammetric precision these effects have not been so well documented – this is one thing I’m working towards within my PhD research. Oftentimes researchers will use JPEGs taken off the camera used, which can have custom filters applied prior to use, making reporting and replication more difficult. If we need to compare research done with different equipment under various lighting conditions on various days, this is one part of the research workflow which is crying out for standardization, as the effects, at least in the case of this one simple example, are clear.

For a visualization of a stack of every quality setting for the first set of crops, please visit this link to my website.