DSP - Rounding and Truncation Quantization

  • Thread starter Thread starter DiamondV
  • Start date Start date
  • Tags Tags
    Dsp Quantization
Click For Summary
SUMMARY

This discussion focuses on the concepts of rounding and truncation quantization in Digital Signal Processing (DSP). The participant explains that quantization involves dividing the range of a signal into L levels, with Delta defined as (max-min)/L. Key insights include the observation that rounding quantization results in a flattened graph at zero, while truncation does not. The discussion also highlights the derivation of the signal-to-quantization noise ratio, assuming the quantization error behaves as a continuous uniform random variable.

PREREQUISITES
  • Understanding of Digital Signal Processing (DSP) fundamentals
  • Familiarity with quantization concepts and terminology
  • Knowledge of signal-to-noise ratio calculations
  • Basic mathematical skills for deriving equations
NEXT STEPS
  • Research "Rounding vs. Truncation Quantization in DSP"
  • Study "Signal-to-Quantization Noise Ratio (SQNR) derivation"
  • Explore "Quantization Error Analysis in Digital Signal Processing"
  • Learn about "Uniform Quantization Techniques and Applications"
USEFUL FOR

Students and professionals in Digital Signal Processing, engineers working on signal encoding, and anyone interested in understanding quantization effects on signal quality.

DiamondV
Messages
103
Reaction score
0

Homework Statement


3c2065fa52.jpg

5c423c6aec.png


Homework Equations

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.
 
Physics news on Phys.org
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.
 

Similar threads

  • · Replies 2 ·
Replies
2
Views
2K
  • · Replies 5 ·
Replies
5
Views
2K
  • · Replies 4 ·
Replies
4
Views
3K
  • · Replies 11 ·
Replies
11
Views
13K
Replies
4
Views
2K
Replies
3
Views
1K
  • · Replies 10 ·
Replies
10
Views
3K
  • · Replies 3 ·
Replies
3
Views
4K
  • · Replies 1 ·
Replies
1
Views
4K
Replies
7
Views
8K