A quiz on image representation and processing in software

Adrian F. Clark

1. What range of values can be stored in an unsigned byte?

   • -128 -- +127
   • 0 -- 256
   • -127 -- +127

   • 0 -- 255

   An unsigned byte represents 256 distinct values, namely 0, 1,... 255.

2. Why does EVE use float values for pixels?

   • it supports a wider range of values

   • it reduces the number of places that the effects of rounding have to be considered

   • it should execute faster
   • that's the way images are stored in disk files

   The use of float values in EVE is largely to reduce the effects of rounding: there are fewer places in a typical piece of code where the effects of rounding need to be considered. There are other advantages too, such as representing a widrer range of values but the rounding one is the most important.

3. Why is imagery with 8 bits per pixel commonly used in vision software?

   • 8-bit values are faster to process than integers
   • computers don't have enough memory to use more than 8 bits/pixel
   • 8-bit values fit into a byte

   • the human eye sees less than 8 bits worh of grey levels

   The underlying reason is that the eye sees less than 256 shades of grey, though they are not evenly spaced in intensity. It is a convenient coincidence that this matches one byte per pixel. Depending on the hardware involved, 8-bit values are processed at the same speed as integers. Unless it is extremely large (and some of those your lecturer processes are), images are small compared to the size of memory these days.

4. When computing a histogram h, what operation do you do a pixel of value v?

   • h[v] = h[v] + 1

   • h[v] = h[v] - 1
   • h[v] = h[v] + v
   • h[v] = h[v] - v

   You're counting the number of times v occurs in the image, so you add unity to the current value of h[v].

5. If a histogram has most of its values bunched together at its left, how does the image appear?

   • well-exposed
   • equally-exposed
   • over-exposed

   • under-exposed

   If the majority of pixels are are the left of a histogram, they have low values and so the image is dark and that normally means it is under-exposed.

6. How does histogram equalization work?

   • histogram bins with low occupancy are doubled

   • histogram bins with low occupancy are spread out

   • histogram bins with low occupancy are merged
   • histogram bins with low occupancy are deleted

   Histogram equalization spreads out low-occupancy parts of a histogram. It is inhereently non-linear in nature and generally a very silly thing to do for computer vision. Your lecture can count on the fingers of one foot the number of times it has helped him develop vision algorithms.

7. The correlation coefficient between two images tells us

   • the amount of noise in the images
   • whether or not two images are identical
   • whether there are gain or offset changes between them

   • how similar they are, ignoring gain or offset changes

   Correlation is a measure which is independent of the means of signals and is normalized by the variances of the two signals, so it is also independent of their relative scales -- it measures only whether the variations in two images relative to their means are in step. The correlation coefficient lies in the range -1 -- +1 and the value describes the similarity of the two images.

8. Why would you contrast-stretch an image?

   • to equalize the image's histogram

   • to spread out the grey levels which are occupied

   • to improve its exposure
   • to improve subsequent processing

   The real purpose of contrast-stretching an image is to improve its appearance when seen by a human, with a wider range of grey levels being occupied. This doesn't actually improve its exposure because if only 10 grey levels were occupied before stretching, only ten would be afterwards too, it's just that they are further apart. There are a few cases in which contrast-stretching might help processing (morphology springs to mind) but that's rarely the case. Contrast stretching definitely does not equalize the histogram!

9. If using histograms to perform content-based image retrieval, which of the following is suitable for determining similarity?

   • the square

   • the cross-correlation

   • the difference
   • the product

   One can use the sum-squared difference but not the difference, product or square. The cross-correlation, however, does determine similarity.

10. What is plotted along the abscissa (x-axis) of a histogram?

    • mean value

    • grey level

    • standard deviation
    • number of occurrences

    The x-axis of a histogram plots the grey level; the y-axis plots the number of occurrences of that grey level.