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The fractional -bit two's complement number representation evenly distributes quantization levels between and . The spacing between quantization levels is then Any signal value falling between two levels is assigned to one of the two levels.
is our notation for quantization. is then the quantization error.
One method of quantization is rounding , which assigns the signal value to the nearest level. The maximum error is thus .
Another common scheme, which is often easier to implement in hardware, is truncation . assigns to the next lowest level. The worst-case error with truncation is , which is twice as large as with rounding. Also, the error is always negative, so on average it may have a non-zeromean (i.e., a bias component).
Overflow is the other problem. There are two common types: two's complement (or wraparound ) overflow, or saturation overflow. Obviously, overflow errors are bad because they are typically large ; two's complement (or wraparound) overflow introduces more error than saturation, but is easierto implement in hardware. It also has the advantage that if the sum of several numbers is between , the final answer will be correct even if intermediate sums overflow! However, wraparound overflow leaves IIR systemssusceptible to zero-input large-scale limit cycles, as discussed in another module. As usual, there are many tradeoffs to evaluate, andno one right answer for all applications.
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