Skip to content Skip to navigation


You are here: Home » Content » Analog-to-Digital Conversion


Recently Viewed

This feature requires Javascript to be enabled.


(What is a tag?)

These tags come from the endorsement, affiliation, and other lenses that include this content.

Analog-to-Digital Conversion

Module by: Don Johnson. E-mail the author

Summary: Analog-to-digital conversion.

Note: You are viewing an old version of this document. The latest version is available here.

Amplitude Quantization

The Sampling Theorem says that if we sample a bandlimited signal st s t fast enough, it can be recovered without error from its samples sn T s s n T s , n=101 n 1 0 1 Sampling is only the first phase of acquiring data into a computer: Computational processing further requires that the samples be quantized: analog values are converted into digital form. In short, we will have performed analog-to-digital (A/D) conversion.

Figure 1: A three-bit A/D converter assigns voltage in the range 11 1 1 to one of eight integers between 0 and 7. For example, all inputs having values lying between 0.5 0.5 and 0.75 0.75 are assigned the integer value six and, upon conversion back to an analog value, they all become 0.625 0.625 . The width of a single quantization interval ΔΔ equals 22B 2 2B . The bottom panel shows a signal going through the analog-to-digital, where BB is the number of bits used in the A/D conversion process (3 in the case depicted here). First it is sampled, then amplitude-quantized to three bits. Note how the sampled signal waveform becomes distorted after amplitude quantization. For example the two signal values between 0.5 0.5 and 0.75 0.75 become 0.625 0.625 . This distortion is irreversible; it can be reduced (but not eliminated) by using more bits in the A/D converter.
(a) (b)
Figure 1(a) (sys8.png) Figure 1(b) (blank.png)

A phenomenon reminiscent of the errors incurred in representing numbers on a computer prevents signal amplitudes from being converted with no error into a binary number representation. In analog-to-digital conversion, the signal is assumed to lie within a predefined range. Assuming we can scale the signal without affecting the information it expresses, we'll define this range to be 1 1 1 1 . Furthermore, the A/D converter assigns amplitude values in this range to a set of integers. A B B -bit converter produces one of the integers 012B1 0 1 2 B 1 for each sampled input. (Reference) shows how a two-bit A/D converter assigns input values to the integers.

We define a quantization interval to be the range of values assigned to the same integer. Thus, for our example three-bit A/D converter, the quantization interval Δ Δ is 0.25 0.25 ; in general, it is 22B 2 2 B .

Exercise 1

Recalling the plot of average daily highs in Problem FIX ME why is this plot so jagged? Interpret this effect in terms of analog-to-digital conversion.


The plotted temperatures were quantized to the nearest degree. Thus, the high temperature's amplitude was quantized as a form of A/D conversion.

Because values lying anywhere within a quantization interval are assigned the same value for computer processing, the original amplitude value cannot be recovered without error. Typically, the D/A converter, the device that converts integers to amplitudes, assigns an amplitude equal to the value lying halfway in the quantization interval. The integer 6 would be assigned to the amplitude 0.625 0.625 in this scheme. The error introduced by converting a signal from analog to digital form by sampling and amplitude quantization then back again would be half the quantization interval for each amplitude value. Thus, the so-called A/D error equals half the width of a quantization interval: 12B 1 2 B . As we have fixed the input-amplitude range, the more bits available in the A/D converter, the smaller the quantization error.

To analyze the amplitude quantization error more deeply, we need to compute the signal-to-noise ratio, which equals the ratio of the signal power and the quantization error power. Assuming the signal is a sinusoid, the signal power is the square of the rms amplitude: powers=122=12 power s 12 2 12 . The following figure details a single quantization interval.

Figure 2: A single quantization interval is shown, along with a typical signal's value before amplitude quantization snTss nTs and after Qsn T s Q s n T s . ε ε denotes the error thus incurred.
figure_2 (blank.png)

Its width is ΔΔ and the quantization error is denoted by εε. To find the power in the quantization error, we note that no matter into which quantization interval the signal's value falls, the error will have the same characteristics. To calculate the rms value, we must square the error and average it over the interval.

rmsε=1ΔΔ2Δ2ε2d ε =Δ21212 rms ε 1 Δ ε Δ 2 Δ 2 ε 2 Δ 2 12 1 2
Since the quantization interval width for a BB-bit converter equals 22B=2(B1) 2 2B 2 B1 , we find that the signal-to-noise ratio for the analog-to-digital conversion process equals
(SN)R=122(2(B1))12=3222B=6B+10log1015 dB; SN R 12 2 2 B1 12 32 2 2B 6B 10 101 5 dB ;
Thus, every bit increase in the A/D converter yields a 6 dB increase in the signal-to-noise ratio.

Exercise 2

This derivation assumed the signal's amplitude lay in the range 1 1 1 1 . What would the amplitude quantization signal-to-noise ratio be if it lay in the range A A A A ?


Exercise 3

How many bits would be required in the A/D converter to ensure that the maximum amplitude quantization error was less than 60 db smaller than the signal's peak value?


Solving 2B=.001 2 B .001 results in B=10 B 10  bits.

Exercise 4

Music on a CD is stored to 16-bit accuracy. What signal-to-noise ratio does this correspond to?


Once we have acquired signals with an A/D converter, we can process them using digital hardware or software. It can be shown that if the computer processing is linear, the result of sampling, computer processing, and unsampling is equivalent to some analog linear system. Why go to all the bother if the same function can be accomplished using analog techniques? Knowing when digital processing excels and when it does not is an important issue.

Content actions

Download module as:

Add module to:

My Favorites (?)

'My Favorites' is a special kind of lens which you can use to bookmark modules and collections. 'My Favorites' can only be seen by you, and collections saved in 'My Favorites' can remember the last module you were on. You need an account to use 'My Favorites'.

| A lens I own (?)

Definition of a lens


A lens is a custom view of the content in the repository. You can think of it as a fancy kind of list that will let you see content through the eyes of organizations and people you trust.

What is in a lens?

Lens makers point to materials (modules and collections), creating a guide that includes their own comments and descriptive tags about the content.

Who can create a lens?

Any individual member, a community, or a respected organization.

What are tags? tag icon

Tags are descriptors added by lens makers to help label content, attaching a vocabulary that is meaningful in the context of the lens.

| External bookmarks