DSP - Rounding and Truncation Quantization

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Digital Signal Processing involves sampling signals with infinite precision, which necessitates quantization into finite amplitude values. Quantization divides the signal range into discrete levels, determined by the formula Delta = (max-min)/L. The discussion highlights rounding and truncation quantization methods, noting that rounding results in a flattened error graph at zero, while truncation does not. The quantization error can be modeled as a continuous uniform random variable within each quantization interval. Understanding these concepts is crucial for analyzing signal-to-quantization noise ratios effectively.
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Homework Statement


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The Attempt at a Solution


Alright, so far what I know about Digital Signal Processing is that you first sample the values of the signal with infinite precision/infinite amplitude values and of course you can't encode these values, so you need to quantize these values into finite set of amplitude values. So you divide the distance between the max and min of the signal into L zones(L quantification levels) each having a height of Delta. So Delta = (max-min)/L

Now my lecture notes go on to rounding and truncation quantization, but these are the only slides given for them(shown above). I've searched online for rounding and truncation quantification and I can't find much. Can anyone explain the graphs to me and how the quantization error is gotten?

I can see that with the rounding graph that is flattens at 0 and with the truncation one it doesnt.
 
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You can derive the signal to quantization noise ratio by assuming the error is a continuous uniform random variable in each interval. You might try doing a search on signal to quantization noise.
 

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