More than just an effect
Deep Picture is a method of conversion, that keeps a choosen part in high resolution while making the pixels grow, the more you move away from the detailed part. The growth of the single pixels from one row to the next can be adjusted precisely. This method not only creates a special new effect, it can also save lots of memory. This lets you open big pictures more quickly, makes websites load faster and lets you store a bigger amount of pictures on devices with small memory.
I differentiate mainly between a linear and an exponential pixel growth.
Try to imitate this effect with an existing picture editing tool - you'll fail, this is all new!
Update of the Prototype
The colouring has been improved, it is now exactly calculated. Also exponential growth is now possible. On the upper panorama you can see an example of exponential pixel-growth. On the one below the old, linear growth with its different character.
A panorama like this easily shows the difference between the two options that are already possible.
The colouring
Beneath this text you can find the comparism of the three possible colouring options. They can be calculated by examining the RGB-values, picked by determining the most occuring colour or randomly taken from the mother picture - which creates different results for each conversion.
Random occuring colour
Most frequent colour
Average colour (RGB-calculation)
Original
PNG - 61,6 MB; JPG - 13,7 MB; HEIF - 11,9 MB
Save Memory
The aesthetical sense or nonsense of the converter is a question of personal view. But what is with the technical question? I have often talked about the possibility of saving lots of memory space, what could especially be useful when dealing with larger pictures or e.g. videos, but how are the facts?
I took a 10'000 x 5'000 pixels example picture and converted it into many different types of files. You can see Frankfurt (City in Germany), it's the view from the Main-river. The picture is lizense free and was taken from pexels.com.
Also mind ''LGBTQ+'' in the download section of this website - An 50'000 x 6'260 pixels example to show how much ''.heif'' comprimizes
Just these days I have discovered the advantages of the HEIF-file type for usage in combination with the deep-picture converter. When dealing with blank photographs, you won't see much difference in efficiency compared to jpg. But when dealing with deep-pictures and their characteristic squares, the HEIF format beats all. It is a lossy compression, but all of the details are being kept, the boat and it's name are as clear as in the original and compared to the converted deep-picture PNG file the human eye will hardly find a difference.
On the upper picture you can see the original file, while the one below shows an exponential deep-picture of the photo.
Deep Picture
PNG - 5,36 MB; JPG - 2,86 MB; SVG - 8,44 MB; HEIF - 519 kb
Keep the general view
The effect doesn't have to dissolve everything inside a pixel-matrix. A general resolution, that still keeps the picture high resoluted on most devices, can be kept easily.
Keep the details
There are a lot of pictures, where there is actually just one tiny part that consumes all of the viewers attention. These parts are the only ones that users tend to zoom in. So why should the whole picture be highly resoluted? And even if your not interested in saving space, which may be not so important when dealing with small pictures, you can set an excellent focus. Secondary stuff is beeing thrown out.
No sharp edges
A Deep Picture isn't just splitted into high and low resoluted disconnected parts, the borders are smooth. The pixels grow steadily from the center to the pictures corners. One can choose between linear and exponential growth (This one is exponential). For both of these options, the pixel-growth can be adjusted very precisely. Is there a point, in which the resolution suddenly changes? - No, everything is smooth! In the example below you can see the net structure, that is because I took a snapshot of the SVG-file.
The linear net-structure
Here you can see the linear net-structure. Row by row the pixels grow by an absolute value, in this case it is ''1''. This algorithm is possible since the first prototype got finished on the 27th of October, 2021.
The exponential net-structure
Here you can see how the exponential net-structure is beeing built up. Every of those little black fields is creating a coloured ''pixel'' in the converted Deep-Picture. I like this one more than the linear structure, it is more beautiful and creates more useful results, how I think.
Extend the high resoluted area
Of course the center doesn't have to be a single pixel, it can be extended as much as the user want's. In this case it is important to see all of the motorbike in high resolution, so the pixel growth starts beyond that area. Here I have choosen the linear structure for conversion.
''Deep-Stitching''
The possibility of creating one big image out of several photographs with different zoom-factor could also be interesting in combination with this picture converter. A photographer could shoot let's say ten photographs while only adjusting the zoom-factor, then these photos are stitched, while unnecessary information is thrown out and only the clearest details and the widest angles are being stored. No detail will show up twice (because there will only be one file then).
A photo that is created that way (with all of its higher and lower resoluted parts) should then be thrown into the Deep-Picture Converter, to create smooth transitions and to further reduce the file size.
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Create an artwork
As you may have already been reading, you can create fantastic artwork when using the transformation in a useful way. Beauty lies in the eye of the viewer, some people think this is cool - some don't. Especially fans of pixel art seem to like this very much, as I noticed when presenting it to the social networks.
There is more
