Spatially invariant systems

I was watching this video on spatially invariant systems:

And I found the definition a bit confusing, or seems vacuous, because isn't $T[x(n_1 - k_1, n_2 - k_2)] = y(n_1 - k_1, n_2 - k_2)$ always true by definition?

• This is the problem with the notational convention in signal processing... Here $x(n_1,n_2)$ doesn't refer to the value of $x$ at a specific point $(n_1,n_2)$ but to the entire signal $x$. So $x(n_1-k_1,n_2-k_2)$ means the signal $x$ translated by $(k_1,k_2)$. The transformation $T$ acts on the signal as a whole, not pointwise. – Rahul Dec 31 '16 at 16:27
• Could you elaborate in an answer with some examples? Thanks! – qed Dec 31 '16 at 17:16

Spatial invariance simply means that shifting the input signal results in an equally shifted output signal. So if the response to an arbitrary signal $x(n_1,n_2)$ is $y(n_1,n_2)$, and the response to a shifted version of the same input signal $x(n_1-k_1,n_2-k_2)$ is $\tilde{y}(n_1,n_2)$, then the system is shift invariant if and only if

$$\tilde{y}(n_1,n_2)=y(n_1-k_1,n_2-k_2)\tag{1}$$

Any system that can be described by the following convolution sum is shift-invariant:

$$y(n_1,n_2)=\sum_{l_1}\sum_{l_2}h(l_1,l_2)x(n_1-l_1,n_2-l_2)\tag{2}$$

where $h(n_1,n_2)$ is the system's impulse response.

EDIT: As requested in the comments, here are some simple examples of shift-variant systems:

1. $y(n_1,n_2)=x(2n_1,n_2)$
2. $y(n_1,n_2)=n_1\cdot x(n_1,n_2)$
3. $y(n_1,n_2)=x(-n_1,-n_2)$

Let's look at the examples above in more detail:

1. \begin{align*} y(n_1 - d_1, n_2 - d_2) &= x(2(n_1 - d_1), n_2 - d_2) \\ & \ne x(2n_1 - d_1, n_2 - d_2) \end{align*}

2. \begin{align*} y(n_1 - d_1, n_2 - d_2) &= (n_1 - d_1) & \cdot x(n_1 - d_1, n_2 - d_2) \\ &\ne n_1 & \cdot x(n_1 - d_1, n_2 - d_2) \end{align*}

3. \begin{align*} y(n_1 - d_1, n_2 - d_2) &= x(-(n_1 - d_1), -(n_2 - d_2)) \\ & \ne x(-n_1 - d_1, -n_2 - d_2) \end{align*}

• Could you add an example? Thanks! – qed Jan 1 '17 at 13:04
• @qed: Have a look at my edited answer. – Matt L. Jan 1 '17 at 13:17
• Ok, what is $\tilde{y}$ in your example? – qed Jan 1 '17 at 13:42
• @qed: As I said in my answer, it's the response to a shifted version of the input signal. Since any system described by the convolution sum (2) is shift-invariant, the condition (1) is satisfied, i.e., $\tilde{y}$ equals the shifted response. – Matt L. Jan 1 '17 at 13:48
• There are some confusions here. (1) I assume the $k_1, k_2$ used in the summation are quite different from the $k_1, k_2$ used for shifting? (2) There seems to be two interpretations of $y$ here: either as a function of $n_1, n_2$ or as a function of $x(n_1, n_2)$. The second interpretation is not defined, hence the difference between the two is unclear. – qed Jan 1 '17 at 13:57

My answer is too late but anyway ...

I was watching this exact slide and got as confused as you! Then I came across your question. I don't fully get Matt L's answer. After some thinking I realized that the main cause of the confusion is how we view the system $$T[]$$.

I came up with the following explanatory example:

If $$T[x(n)]=nx(n)$$, what does it mean? It means that the system will multiply the input by $$n$$. Thus, if we give the system another input function $$z(n)$$, it will output $$nz(n)$$.

Thus:

For $$x(n)=n$$, $$T[x(n)]=nx(n)=n\cdot n$$.

For $$z(n)=n-\tau$$, $$T[z(n)]=nz(n)=n\cdot(n-\tau)$$.

In addition, if $$T[]$$ is spatially invariant, then $$T[x(n-\tau)]=(n-\tau)(n-\tau)$$.

However, note that $$z(n)$$ is indeed $$x(n-\tau)$$, and the output is $$n\cdot(n-\tau)$$ rather than $$(n-\tau)(n-\tau)$$. Thus, $$T[]$$ is spatial variant.

In general, if the modification applied by the system to the input involves the time $$n$$, then the system is spatially varying.