Visualisation Pixels – the Link between Digital Bits and Physical Atoms

Visualisation Pixels –
the Link between Digital Bits
and Physical Atoms

Theoretical Underpinnings
for the Re-Visualisation of Images

Pixels embody Analogue Signals as Digital Building Blocks

Pixels or ‘picture elements’ are for TV and computer screens what bits are for computers and atoms for the physical world: basic building blocks.

The Wikipedia entry tells us that a pixel is the smallest addressable element in an all points addressable display device.

Liquid-crystal display (LCD) pixels are manufactured in two-dimensional grids and are represented as dots or squares. But cathode ray tube (CRT) pixels correspond to their timing mechanisms.

Pixels thus combine the physical characteristic of a building block in screens as hardware with the digital value of being usable by software.

However, our software is unique in showing these digital values such that they offer a wide range of opportunities for scientific and commercial applications.

Re-Visualised Images reveal New Depths and Structures with ‘Pixel Accuracy’

Our Smart Knowledge Engine re-visualises digital images in a new kind of True Colour 3D with Pixel Accuracy, i.e. every single pixel of an image is shown.

The new visual representation gives us more depth, detail and structure in an image as a whole.

numerical techniques give us not only numerical comparability, but also unambiguous precision and repeatability. The challenge, however, lies in the vastness of systems that provide analogue-to-digital conversion.

This means that the technology needs to be taken into account when trying to agree on definitive standards of quality – whether between technologies, the resolution of the resulting images or the quality and characteristics of what the images represent.

The Variety of Analogue-to-Digital-Conversion [ADC] Technologies as Challenge

The generic nature of our code allows for input of any image, produced by any technology at any scale: microscopes, cameras and telescopes, infrared, WiFi, x-rays, anything goes. For our re-visualisations show simply the numbers that a complex process produces, when translating analogue signals into digital values.

The visualisation of this numerical representation of digital images is a new tool for investigation:

1. Visually, the expert sees more and thus understands more;

  • Experts can then set numerical values as
    • criteria for searching and selecting;
    • boundary conditions to flag events that are ‘off limits’;

2. Numerically, our software can compare images, i.e. with complete precision and in an automated and replicable fashion;

  • This automation of image investigations has particular value for
    • Quality control;
    • Non-destructive testing;
    • Finding ‘reference technologies’ and ‘reference images’ to set new standards.

These ‘subpixels’ are also shown in Wikipedia:
http://www.smart-knowledge-portals.uk/projects/346

The translation of analogue signals into digital values is determined by so many Analogue-to-Digital Converter technologies that it will require the efforts of crowds and Open Data to come to definitive mechanisms of ‘reference translation’. It seems that technologies are ‘externalised reflections’ of our minds that each have their own ways of translating perceptions of the outer world into interpretations and understanding. But digitally, All is Number and visually, Everything is Light and Colour.

Analogue Signals are Continuous in Time and Space

In its collective wisdom, Wikipedia describes various sources of analogue signals that may exist: voltage, current or frequency of electric signals or sound, light, temperature and pressure for example.

From a mathematical perspective, analogue signals have a theoretically infinite resolution, as they can propagate into the eternity of time and the infinity of space. According to Wikipedia, their primary disadvantage is noise which shows up as ‘hiss’ in audio and ‘snow’ in video signals.

Electronics also includes digital signals, but my thinking is rooted in the abstraction of digital data and numbers as digital values. It resulted in software specifications, learning from using my software and the realisation that the preparation of data is as important as the coding of software to produce our new visualisation styles.

Digital Values are Discrete in Data Bases and Spreadsheets

In contrast to analogue signals, digital signals and values are finite and discrete. These terms are common to Data Science as well as mathematics. However, Data Science has been popularised by commercial companies that focus on the metrics of Big Business data, whereas metrology, the science of measurement, is a deeply scientific concern that Wikipedia helps to demystify, too:

  1. The definition of internationally accepted units of measurement;
  2. The realisation of these units of measurement in practice;
  3. Traceability, linking measurement made in practice to reference standards.

The sub-fields deriving from this are:

  1. Scientific or fundamental metrology;
  2. Applied, technical or industrial metrology;
  3. Legal

To complement this explanation, metric or metrical may refer to:

  1. Mathematics – the notion of distance in metric space;
  2. Physics – the ‘home’ of the International System of Units or metric system;
  3. Computer science;
  4. Performance metric – a measure of an organisation’s activities and performance.

According to Wikipedia, performance metrics relate to safety, time, cost, resources, scope, quality and actions, and the development of these metrics usually follows these steps:

  1. Establishing critical processes / customer requirements;
  2. Identifying specific, quantifiable outputs of work;
  3. Establishing targets against which results can be scored.

Measuring is a Matter of Units, Scale and Technologies

How come I dreamt up 3D Metrics as a trade and domain name? Here are my basic definitions:

  1. using Rene Descartescoordinate system, the nature of reality is fundamentally 3D, as far as Space is concerned;
  2. on screen, I have replaced Time as the 4th dimension by ‘visual z’ as a 4th axis for visualising images or time dependent data;
  3. the numerical representation of 2D images offers us new quantifications of light and colour for the ‘momentary reflections’ of 3D realities;
  4. we call the quantification Digital Colour Brightness as a generic measurement or technology-dependent reference standard;
  5. the measuring units that Digital Colour Brightness quantifies need to be defined by domain experts because only they know what the images represent and what they are looking to measure and, above all, compare over time.

Measuring is a ‘composite’ of a number and of measuring units. The number is a quantification, not quantization. Measuring units are meter, kilogram, second in the physical world – as the basis for a host of derived units.

But the commercial world needs metrics that are simpler than statistics from the analysis of data.

Dots are for Patterns in our Eyes what Pixels are for Images: Visualisation Units

Digital Camera Sensors Explained is an excellent technical article on What Digital Camera. It illustrates how the digital world emulates our human ability to spot dots and join them into patterns. Cameras use:

  • sensors that physically join light and colour:
    • Active Pixel Sensors (APS) or
    • Complementary Metal-Oxide-Semiconductors (CMOS)
  • and mathematically translate electromagnetic frequencies into digital pixel values:

As analogue devices, the sensors pick up electric charges that need to be amplified before being translated into digital values. A number of different filters are used to separate and integrate colour on top of shades of greyscales between Black and White.

The process from capturing images to reproducing them on different size and quality screens is thus a very complex one and it seems to be an utter miracle that it can happen at the speed that it does.

For the technology of turning light and colour into digital values is at least as complex as turning digital values back into light and colour such that the screen picture resembles the reality captured.

But just as dots form patterns in our minds, thanks to our eyes as ‘sensors’ and ‘filters’, so do bits form the digital pixels and physical building blocks as ‘visualisation units’.

Visualisations stem from: Digital Pixel Values IN, Images on Screen Pixels OUT

If we want to investigate the differences between ‘3D realities’ and ‘2D reproductions’, we thus need to study:

1. the input process of producing digital values:

  • the analogue-to-digital conversion technique used:
    • the resolution in time and space;
    • the accuracy of recording;
    • the number of bits as output;

2. the output technology of using pixels generally based on Liquid Crystal Displays:

  • pixels consisting of a layer of molecules aligned between two transparent electrodes and two filters polarising light;
    • liquid crystal is the 4th state of matter in addition to solid, liquid and gaseous, discovered in 1888 by an Austrian botanist, a German physicist and a microscope;
    • the German word Schlieren is used to describe the streaky texture of one of the liquid crystal phases called nematic;
  • this article on Liquid-crystal displays says “it is understood that most liquid crystals, like cholesteryl benzoate, consist of molecules with long, rod-like structures. It is the combination of the attractive forces that exist between all molecules coupled with the rod-like structure that causes the liquid-crystal phase to form.”

1. the software process of visualising numbers:

  • based on my mathematical spaces and transformations, I wrote my first prototype myself and produced this screenshot which made me think that I’m stepping along the footsteps of DNA;

2. a second prototype was produced for me offline based on my specifications;

3. the third prototype is currently work in progress online and produced the above screenshots.

Applying our Smart Knowledge Engine as a New Instrument of Investigation

It’s not easy to raise funds or interest potential clients into being first to save money from using one of our three software methods or to invest in research and development. It takes enlightened capitalists and intellectual generosity. Most people who understand the value and appreciate the significance suffer from Not Invented Here jealousy. But I shall forever keep trying my best and keep adding to my box of business cards, LinkedIn connections and web publications.

The Science of Imaging, Materials and Health are my preferred areas of research for the image re-visualisation method.

Open Data relating to climate change and money supply, inflation and national debts are my hobby horses for the layering method.

Measuring the quality of life in Smart Cities and empowering Smart Citizens to contribute their knowhow to solutions and improvements is part of this effort of bringing new and deeper understanding as enlightenment through our new visualisation styles for images, complex and time dependent data.