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This is all very similar to Inter Symbol Interfence in digital comm systems. You don’t want your pixel bleeding into the next, so choosing the appropriate filt

This is all very similar to Inter Symbol Interfence in digital comm systems. You don’t want your pixel bleeding into the next, so choosing the appropriate filter to reduce ISI is important. Typical root raised cosine filters are used, as it is a matched filter for itself, and minimizes ISI depending on the bandwidth chosen. I suppose Shannon’s capacity theorem can be applied to images, trading off bandwidth for SNR. If resolution is bandwidth, and brightness the SNR, what would capacity be in a photo? The sharpness? That would imply sharper photos would be better off with higher pixel sensors than with dynamic range. You can always decimate to increase SNR. In electronics, it’s easier to make faster converters than increase dynamic range, so oversample and decimate. Same probably with image sensors.

theoh 8y ago
Um, the first point, in digital imaging, is that a pixel is not a little square. It's a sample. So it doesn't have any extension to blur. Any filtering invariably happens either at sampling or at reconstruction. https://graphics.stanford.edu/courses/cs248-04/ho6/ https://graphics.stanford.edu/courses/cs248-04/ho6/ Of course sharpness is a function of resolution. I'm not sure it makes sense to talk about capacity, because typically the resolution of the image is non-negotiable. The number of bits needed to encode it is variable and depends on compression technique used. So in general, Shannon-style channel thinking operates on a different layer from the sampling and reconstruction processes we are discussing here. It's more relevant to how discrete pixel values are coded, or more elaborate compression mechanisms which exploit coherence in the image.
dreamcompiler 8y ago
> I suppose Shannon’s capacity theorem can be applied to images, trading off bandwidth for SNR. If resolution is bandwidth, and brightness the SNR, what would capacity be in a photo? The sharpness? That would imply sharper photos would be better off with higher pixel sensors than with dynamic range. To perform this tradeoff and capture e.g 1-bit images with super-high resolution you'd need the sensor to do error diffusion a la sigma-delta modulation, and AFAIK image sensors cannot currently do this.
- madengr 8y ago
  Ah, so you are saying the quantanization noise can't be averaged out, as with decimation after an ADC.
  - dreamcompiler 8y ago
    It could, but you'd have to start with a sensor that had both a high number of bits per sample and super-high spatial frequency. If you had such a sensor, there would be no point in decimating; you'd just use the samples the sensor gave you directly. The point with decimating is to sacrifice some spatial resolution for an increase in bits-per-sample from a low-bps (but high-frequency) sensor. This is done routinely for audio signals but it would be a lot more tricky with a two-dimensional signal. Not impossible, but it would require processing elements between the pixels to diffuse the error properly without losing information.

theoh 8y ago

Um, the first point, in digital imaging, is that a pixel is not a little square. It's a sample. So it doesn't have any extension to blur. Any filtering invariably happens either at sampling or at reconstruction. https://graphics.stanford.edu/courses/cs248-04/ho6/ https://graphics.stanford.edu/courses/cs248-04/ho6/ Of course sharpness is a function of resolution. I'm not sure it makes sense to talk about capacity, because typically the resolution of the image is non-negotiable. The number of bits needed to encode it is variable and depends on compression technique used. So in general, Shannon-style channel thinking operates on a different layer from the sampling and reconstruction processes we are discussing here. It's more relevant to how discrete pixel values are coded, or more elaborate compression mechanisms which exploit coherence in the image.

dreamcompiler 8y ago

> I suppose Shannon’s capacity theorem can be applied to images, trading off bandwidth for SNR. If resolution is bandwidth, and brightness the SNR, what would capacity be in a photo? The sharpness? That would imply sharper photos would be better off with higher pixel sensors than with dynamic range. To perform this tradeoff and capture e.g 1-bit images with super-high resolution you'd need the sensor to do error diffusion a la sigma-delta modulation, and AFAIK image sensors cannot currently do this.

madengr 8y ago
Ah, so you are saying the quantanization noise can't be averaged out, as with decimation after an ADC.
- dreamcompiler 8y ago
  It could, but you'd have to start with a sensor that had both a high number of bits per sample and super-high spatial frequency. If you had such a sensor, there would be no point in decimating; you'd just use the samples the sensor gave you directly. The point with decimating is to sacrifice some spatial resolution for an increase in bits-per-sample from a low-bps (but high-frequency) sensor. This is done routinely for audio signals but it would be a lot more tricky with a two-dimensional signal. Not impossible, but it would require processing elements between the pixels to diffuse the error properly without losing information.