pixell | rectangular pixel map manipulation and harmonic analysis | Computer Vision library

Note: my question is very specific, apologies if the title isn't clear enough as to what the problem is.

I'm creating a pixel art editor application using Canvas, and the pixel art data is saved into a Room database.

Here's the canvas code:

I'd like to efficiently create an up-scaled CIImage from a minimally sized one, using Nearest Neighbour scaling.

Say I want to create an image at arbitrary resolutions such as these EBU Color Bars:

In frameworks like OpenGL, we can store this a tiny 8x1 pixel texture and render it to arbitrary sized quads, and as long as we use Nearest Neighbour scaling the resulting image is sharp.

Our options with CIImage appear to be limited to .transformedBy(CAAffineTransform(scaleX:y:)) and .filteredBy(filterName: "CILanczosScaleTransform") which both use smooth sampling, which is a good choice for photographic images but will blur edges of line art images such as these color bars - I specifically want a pixellated effect.

Because I'm trying to take advantage of GPU processing in the Core Image backend, I'd rather not provide an already upscaled bitmap image to the process (using CGImage, for example)

Is there some way of either telling Core Image to use Nearest Neighbour sampling, or perhaps write a custom subclass of CIImage to achieve this?

I am making a program where I want to take an image and reduce its color palette to a preset palette of 60 colors, and then add a dithering effect. This seems to involve two things:

• A color distance algorithm that goes through each pixel, gets its color, and then changes it to the color closest to it in the palette so that that image doesn't have colors that are not contained in the palette.
• A dithering algorithm that goes through the color of each pixel and diffuses the difference between the original color and the new palette color chosen across the surrounding pixels.

After reading about color difference, I figured I would use either the CIE94 or CIEDE2000 algorithm for finding the closest color from my list. I also decided to use the fairly common Floyd–Steinberg dithering algorithm for the dithering effect.

Over the past 2 days I have written my own versions of these algorithms, pulled other versions of them from examples on the internet, tried them both first in Java and now C#, and pretty much every single time the output image has the same issue. Some parts of it look perfectly fine, have the correct colors, and are dithered properly, but then other parts (and sometimes the entire image) end up way too bright, are completely white, or all blur together. Usually darker images or darker parts of images turn out fine, but any part that is bright or has lighter colors at all gets turned up way brighter. Here is an example of an input and output image with these issues:

Input:

]3

Output:

I do have one idea for what may be causing this. When a pixel is sent through the "nearest color" function, I have it output its RGB values and it seems like some of them have their R value (and potentially other values??) pushed way higher than they should be, and even sometimes over 255 as shown in the screenshot. This does NOT happen for the earliest pixels in the image, only for ones that are multiple pixels in already and are already somewhat bright. This leads me to believe it is the dithering/error algorithm doing this, and not the color conversion or color difference algorithms. If that is the issue, then how would I go about fixing that?

Here's the relevant code and functions I'm using. At this point it's a mix of stuff I wrote and stuff I've found in libraries or other StackOverflow posts. I believe the main dithering algorithm and C3 class are copied basically directly from this Github page (and changed to work with C#, obviously)

I am trying to get pixel intensity values from regions of interest in RGB images.

I segmented the image and saved the regions of interest (ROI) using regionprops 'PixelList' in MATLAB, as shown below:

In this example I am using "onion.png" image built in MATLAB. (But in reality I have hundreds of images, and each of them have several ROIs hence why I'm saving the ROIs separately.)

I am using CIImage to add a number of different filter types to an image. All filters are working fine with their default values, plus the CIPixellate and CICrystallize filters are working with kCIInputScaleKey and kCIInputRadiusKey values added respectfully. Howsever I am having trouble adding values for the CILineOverlay filter. I would like to feed it a specific value for inputEdgeIntensity. The Core Image Filter Reference docs state:

inputEdgeIntensity: An NSNumber object whose attribute type is CIAttributeTypeScalar and whose display name is Edge Intensity.

Default value: 1.00

But I can't find an example anywhere of how to add this value using swift. Using this code does not work:

First, I read the cover image as greyscale image.

I am new into programming and want to sort the entries in my list allIetsDatalight by timestamp included in names of the entry. So I put all the files, which have following namestructure 125_L_2020-11-12_12-08-35.IV2, of my path with the ending .IV2 in the list allIetsDatalight and now I want to sort all entries. Another problem is, that the first 6 characters of my filename 125_L_ always changes from file to file. I struggling since two days two find something in the internet, but i couldn´f find something right for my program.

My Program:

I have an image that I initialized to be all 0 values.

Experimenting with ggplot2 I noticed a difference in the graphical output between geom_bar/geom_col and geom_linerange. As soon as I use these functions in combination with coord_polar (to create pie or donut charts) the first two outputs are pixellated whereas geom_linerange produces smooth lines.

I am fine with that. Still I wonder why and where in the process of creating the output this difference occures?