3 ms·

I think, a better way of putting it may be, pixels are samples at points, rather than representing actual point samples. Meaning, while they are sampled at a sp

I think, a better way of putting it may be, pixels are samples at points, rather than representing actual point samples. Meaning, while they are sampled at a specific location, these are not actual values, as this would require ideal sensor technology that will probably never be achieved. So we have to account for all kinds of diffusion, reflection, sensor surfaces, shapes of optical channels, fall-off thereof, etc – and may wonder (probably to no definitive end) at what shape and to what extent to model them best. Edit: Moreover, even when it comes to simply filling an area, things aren't that simple. E.g., when this was written (1995), this was still the era of CRTs, and CRTs are analog devices with analog response. A sole pixel may never reach full phosphor activation, while a stretch of them will, and there are also signal flanks. If we wanted to represent a scanline information as 0.0 1.0 1.0 1.0 0.5 0.5 we may have to actually write for a true representation (letting go of the idea of full phosphor activation), 0.0 1.0 0.8 0.8 0.0 0.4 So, for this specific purpose, not every pixel is the same, but rather relies on context…

chriswarbo 4y ago
> while they are sampled at a specific location, these are not actual values, as this would require ideal sensor technology that will probably never be achievedwhile they are sampled at a specific location, these are not actual values, as this would require ideal sensor technology that will probably never be achieved Indeed; hence why they're a useful abstraction > So we have to account for all kinds of diffusion, reflection, sensor surfaces, shapes of light channels, fall-off thereof, etc – and may wonder at what shape and to what extent to model them best. Whilst 'shapes' can give particularly simple models (like nearest-neighbour voronoi cells), that's still quite a limiting assumption. For example, models which take multiple neighbours into account, like bicubic interpolation (and pretty much everything except pixel-art editors), don't fit the concept of "shape" very well. In particular, the influence/extent of the pixels "overlap", which requires a separate notion of composition/interaction; and some areas may even have negative influence!
- masswerk 4y ago
  > In particular, the influence/extent of the pixels "overlap", which requires a separate notion of composition/interaction; and some areas may even have negative influence! Indeed! Compare the edit. (Sorry for this other overlap in the temporal domain.)

chriswarbo 4y ago

> while they are sampled at a specific location, these are not actual values, as this would require ideal sensor technology that will probably never be achievedwhile they are sampled at a specific location, these are not actual values, as this would require ideal sensor technology that will probably never be achieved Indeed; hence why they're a useful abstraction > So we have to account for all kinds of diffusion, reflection, sensor surfaces, shapes of light channels, fall-off thereof, etc – and may wonder at what shape and to what extent to model them best. Whilst 'shapes' can give particularly simple models (like nearest-neighbour voronoi cells), that's still quite a limiting assumption. For example, models which take multiple neighbours into account, like bicubic interpolation (and pretty much everything except pixel-art editors), don't fit the concept of "shape" very well. In particular, the influence/extent of the pixels "overlap", which requires a separate notion of composition/interaction; and some areas may even have negative influence!

masswerk 4y ago
> In particular, the influence/extent of the pixels "overlap", which requires a separate notion of composition/interaction; and some areas may even have negative influence! Indeed! Compare the edit. (Sorry for this other overlap in the temporal domain.)