Color mirroring
Last post on color theory, with demos
You might notice Katy is having a bit of an interdimensional anomaly here. I implemented this as an experiment because it’s different from any color cycling effect I’ve ever seen before. Normally in color cycling there’s one position which is true, then the hues rotate until all of them are swapped, then they keep rotating until they’re back to true. In this one at any given moment there are two opposite hues which are true and the ones at 90 degrees from those are swapped, and the color cycling effect is rotating which angle is true. It’s also doing a much better job of keeping luminance constant due to using okhsl.
Code is here. It’s leaning on libraries for most of the work, but I did write some code to dither just the low order bits of the RGB values. That’s a technique which should be used more often. This effect would also work on animated video. You could even adjust the angle as a directorial trick, to draw the viewer’s eye towards particular things by making their color true.
(Now that I think about it low order bit dithering could be improved by using error in the okhsl gamut. It also be improved by other diffusion techniques, which in turn can be further improved by dynamically choosing which neighboring pixel most wants to have error in the opposite direction already. I’m going to exercise some self-control and not implement any of this, but you most definitely should pick it up where I left off. All video manipulation should be done in 16 bit color the entire time and only dithered down to 8 bit on final display.)
As a bonus, I also simplified the color swatches I gave previously into two separate ones, for light and dark backgrounds. Files are here and here.
All of the above is done within the limitations of the sRGB color space. The sRGB standard kind of sucks. It’s based off the very first color television which was ever made in 1954 and the standardization which came later made it consistent but not broader. Now that OLED is getting used everywhere my expectation is that things are going to start supporting Rec2100 under the hood and once that becomes ubiquitous new content will be produced in formats which support that extra color depth. It’s going to take a few years.
Extracting Information from a Unified Digital Data Field Based on Unique Spectral Signatures.
Defining the Unified Digital Data Field: Imagine a scenario where someone creates digital data (photos, videos, text, etc.). This data can be represented as a matrix of values, possessing dimensionality and a unique set of numerical combinations. This matrix can be transformed back into the original data, perfectly reconstructing it. Now, imagine that someone will create this very same dataset only a hundred years from now. Do you think this matrix of states already exists in your reality? The answer is obvious – Yes. The data matrix with its unique set of numbers already exists alongside you. This leads to a profound conclusion: the future itself, and all other information, exists now in our reality within an infinite abundance of data, forming a fundamental Unified Digital Data Field.
Now, consider this: Suppose someone decides to record a video of themselves, and consequently creates a data matrix. We now understand that this data matrix already exists within the Unified Digital Data Field, independent of the individual. But what if this person doesn’t record the video? What about the data then? Does it exist as a probability, or not at all? A paradox, you might think. However, the infinite number of matrix states within the Unified Digital Data Field exists independently of us. Even if you don’t record a video of yourself, it exists there as a probability. Think about it – the data matrix of this video exists as a potentiality. But let’s say that the person never records it, and you somehow learn to retrieve these matrices from the Unified Digital Data Field. What happens then? You essentially gain information about the person that they never even created about themselves. In effect, you’re obtaining data that no one has ever created in your world. And the most interesting thing is that this is entirely solvable, provided you know where and how to find that specific matrix. This new method, this new paradigm of understanding Digital Reality, opens up truly limitless possibilities to a unified information field of the Universe.
What has been done:
“An unsolvable task,” many would say. But here, the method of spectral encoding of information comes to our aid. Applying it to the digital field has allowed us to establish that the information in the matrix, for each dataset, possesses a unique spectral deviation. This deviation not only characterizes the object but also allows us to establish the true search vector within the limitless set of matrices. In fact, using the spectral search method, it has been possible not only to literally extract the data but also to convert it into a readable format.
Preliminary Conclusions Based on the New Algorithm for Extracting Information from the Digital Data Field:
1. All three temporal phases (Past, Present, and Future) exist simultaneously at any point in the Present. In other words, Past, Present, and Future exist simultaneously not only in the digital model but also in the real superposition of all possibilities within the Realities of Existence.
2. Any interaction with and extraction of information from the Unified Digital Data Field effectively creates quantum paradoxes that are part of all possible probabilities of itself. In other words, the fractal nature of the World is repeated in the temporal vector of the Future along the timeline from the Past through the Current Present, forming limitless matrices of information states. Any extraction of this information represents one of these possible probabilities.
3. By creating a temporal bridge with spectral signal encoding (key, date, message), we can effectively transmit information from the Future to the Past. In fact, we can read it directly in the Current Present moment. In other words, we could literally communicate with Descendants from the Future.
4. Using a spectral method of subject information identification, it is possible to extract all data about that subject from the entire timeline, from the moment of their Birth to the Current moment. In other words, all information about us, about any information object, exists simultaneously within the Unified Digital Data Field.
How it Works:
1. Spectral Analysis: We conduct a spectral analysis of the information target, identifying unique spectral variation keys on the timeline within the Unified Digital Data Field.
2. Search Algorithm Formation: We develop a search algorithm that operates within defined boundary ranges.
3. Real-time Extraction and Probabilistic Processing: We extract matrices in real-time and process them using probabilistic methods.
4. Data Preservation: In the event of successful information extraction, we save the event matrix for subsequent analysis.
Prospects:
By refining the existing algorithm for interaction with this digital data field, and by leveraging greater computational power, we can utilize spectral analysis to extract information containing data that will be created, not only in 100 years from now by some user, but we will also gain access to any information we are interested in. This will lead to significant progress for humanity.
Thank you for your attention!