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Upsampling

Upsampling is a science topic covered in the lgStudy science library. This page brings together a partial reference excerpt, illustrations, worked examples, real-world applications and a short study plan, so you can understand Upsampling rather than just read about it. In short: In digital signal processing, upsampling, expansion, and interpolation are terms associated with the process of resampling in a multi-rate digital signal processing system. Upsampling can be synonymous with expansion, or it can describe an entire process of expansion and filtering (interpolation).

Upsampling — main illustration
Upsampling — illustration

Key takeaways

  • Upsampling belongs to science; place it in that map before memorising details.
  • Learn the definition first, then one example that makes the definition concrete.
  • Connect Upsampling to a quantity you can measure, compute or draw — that is where exam questions come from.
  • Reproduce the core statement of Upsampling from memory before moving on to harder problems.

Reference excerpt

In digital signal processing, upsampling, expansion, and interpolation are terms associated with the process of resampling in a multi-rate digital signal processing system. Upsampling can be synonymous with expansion, or it can describe an entire process of expansion and filtering (interpolation). When upsampling is performed on a sequence of samples of a signal or other continuous function, it produces an approximation of the sequence that would have been obtained by sampling the signal at a higher rate (or density, as in the case of a photograph). For example, if compact disc audio at 44,100 samples/second is upsampled by a factor of 5/4, the resulting sample rate is 55,125.

Upsampling by an integer factor Rate increase by an integer factor L {\displaystyle L} can be explained as a 2-step process, with an equivalent implementation that is more efficient:

Expansion: Create a sequence, x L [ n ] , {\displaystyle x_{L}[n],} comprising the original samples, x [ n ] , {\displaystyle x[n],} separated by L − 1 {\displaystyle L-1} zeros. A notation for this operation is: x L [ n ] = x [ n ] ↑ L . {\displaystyle x_{L}[n]=x[n]_{\uparrow L}.}

Interpolation: Smooth out the discontinuities using a lowpass filter, which replaces the zeros. In this application, the filter is called an interpolation filter, and its design is discussed below. When the interpolation filter is an FIR type, its efficiency can be improved, because the zeros contribute nothing to its dot product calculations. It is an easy matter to omit them from both the data stream and the calculations. The calculation performed by a multirate interpolating FIR filter for each output sample is a dot product:

where the h {\displaystyle h} sequence is the impulse response of the interpolation filter, and K {\displaystyle K} is the largest value of k {\displaystyle k} for which h [ j + k L ] {\displaystyle h[j+kL]} is non-zero.

In the case L = 2 , {\displaystyle L=2,} function h {\displaystyle h} can be designed as a half-band filter, where almost half of the coefficients are zero and need not be included in the dot products. Impulse response coefficients taken at intervals of L {\displaystyle L} form a subsequence, and there are L {\displaystyle L} such subsequences (called phases) multiplexed together. Each of L {\displaystyle L} phases of the impulse response is filtering the same sequential values of the x {\displaystyle x} data stream and producing one of L {\displaystyle L} sequential output values. In some multi-processor architectures, these dot products are performed simultaneously, in which case it is called a polyphase filter. For completeness, we now mention that a possible, but unlikely, implementation of each phase is to replace the coefficients of the other phases with zeros in a copy of the h {\displaystyle h} array, and process the x L [ n ] {\displaystyle x_{L}[n]} sequence at L {\displaystyle L} times faster than the original input rate. Then L − 1 {\displaystyle L-1} of every L {\displaystyle L} outputs are zero. The desired y {\displaystyle y} sequence is the sum of the phases, where L − 1 {\displaystyle L-1} terms of the each sum are identically zero. Computing L − 1 {\displaystyle L-1} zeros between the useful outputs of a phase and adding them to a sum is effectively decimation. It's the same result as not computing them at all. That equivalence is known as the second Noble identity. It is sometimes used in derivations of the polyphase method.

Interpolation filter design

Let X ( f ) {\displaystyle X(f)} be the Fourier transform of any function, x ( t ) , {\displaystyle x(t),} whose samples at some interval, T , {\displaystyle T,} equal the x [ n ] {\displaystyle x[n]} sequence. Then the discrete-time Fourier transform (DTFT) of the x [ n ] {\displaystyle x[n]} sequence is the Fourier series representation of a periodic summation of X ( f ) : {\displaystyle X(f):}

When T {\displaystyle T} has units of seconds, f {\displaystyle f} has units of hertz (Hz). Sampling L {\displaystyle L} times faster (at interval T / L {\displaystyle T/L} ) increases the periodicity by a factor of L : {\displaystyle L:}

… excerpt ends here. Continue reading the full article.

Illustrations

Upsampling: Fig 1: Depiction of one dot product, resulting in one output sample (in green), for the case L=4, n=9, j=3. Three conceptual "inserted zeros" are depicted between each pair of input samples. Omitting them from the calculation is what distinguishes a multirate filter from a monorate filter.
Fig 1: Depiction of one dot product, resulting in one output sample (in green), for the case L=4, n=9, j=3. Three conceptual "inserted zeros" are depicted between each pair of input samples. Omitting them from the calculation is what distinguishes a multirate filter from a monorate filter.
Upsampling: Fig 2: The first triangle of the first graph represents the Fourier transform X(f) of a continuous function x(t). The entirety of the first graph depicts the discrete-time Fourier transform of a sequence x[n] formed by sampling the continuous function x(t) at a low-rate of 1/T. The second graph depicts the application of a lowpass filter at a higher data-rate, implemented by inserting zero-valued samples between the original ones.  And the third graph is the DTFT of the filter output. The bottom table expresses the maximum filter bandwidth in various frequency units used by filter design tools.
Fig 2: The first triangle of the first graph represents the Fourier transform X(f) of a continuous function x(t). The entirety of the first graph depicts the discrete-time Fourier transform of a sequence x[n] formed by sampling the continuous function x(t) at a low-rate of 1/T. The second graph depicts the application of a lowpass filter at a higher data-rate, implemented by inserting zero-valued samples between the original ones. And the third graph is the DTFT of the filter output. The bottom table expresses the maximum filter bandwidth in various frequency units used by filter design tools.

Worked examples

Example 1 — a first encounter with Upsampling

Start with the simplest possible case. Write down what Upsampling claims or describes in one sentence, then invent the smallest concrete situation in which that sentence is true. In science, the smallest case is usually a single object, a single equation or a single measurement. Check that every symbol or term in your sentence has a meaning in that case.

Example 2 — changing one variable

Take the situation from Example 1 and change exactly one quantity: double it, halve it, or set it to zero. Predict what should happen to Upsampling before you calculate. Comparing your prediction with the result is the fastest way to find out whether you understand the idea or only the words.

Example 3 — an exam-style question

Typical questions about Upsampling ask you to (a) state it precisely, (b) apply it to given data, and (c) explain a limitation. Practise writing all three answers in under five minutes; the third part is what separates a full-mark answer from an average one.

Applications of Upsampling

In research
Upsampling appears in science research whenever the underlying quantities have to be modelled precisely. Papers usually cite it as a starting assumption and then explore where it breaks down.
In technology and industry
Engineering practice reuses Upsampling in design rules, simulations and safety margins. Knowing the idea lets you read a specification sheet and understand why the numbers look the way they do.
In the classroom
Upsampling is common in secondary-school and first-year university syllabi. It links to neighbouring topics Digital signal processing, Signal processing, so understanding it makes those chapters shorter.
In everyday life
Look for Upsampling outside the textbook — in sport, cooking, traffic, electronics or the sky above you. An example you found yourself is remembered far longer than one you were given.

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How to study Upsampling in 20 minutes

  1. Read the reference excerpt below once, without taking notes.
  2. Close the page and write down what Upsampling means in your own words.
  3. Compare your version with the excerpt and mark what you missed.
  4. Work through the three examples above with pen and paper.
  5. Explain Upsampling out loud to somebody else — or to Teacher Smith in the lgStudy chat.

Frequently asked questions

What is Upsampling in simple terms?

In digital signal processing, upsampling, expansion, and interpolation are terms associated with the process of resampling in a multi-rate digital signal processing system. Upsampling can be synonymous with expansion, or it can describe an entire process of expansion and filtering (interpolation).

Why does Upsampling matter?

Because it connects several science ideas at once: it gives you a definition you can apply, a quantity you can calculate, and a way to check whether a result is plausible.

How should I study Upsampling?

Read the excerpt, restate it from memory, then work through the examples and applications listed on this page. The five-step study plan above takes about twenty minutes.

What does this page cover?

It gives you a compact reference excerpt plus original lgStudy explanations, examples, applications and study material on Upsampling.

Tags

  • Digital signal processing
  • Signal processing

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