Now comes the hard part.

Most of the Math in QR codes in performed in the Galois Field of order 2⁸ = 256. In this set, denoted as GF(256):

• includes the numbers from 0 to 255;
• has an "addition" operation, which is actually the binary XOR and not the usual sum (so the "sum" of two elements will still be part of GF(256));
• has a "multiplication" operation, which is similar to the usual arithmetic multiplication but with some differences so that multiplying two elements will still give us an element of GF(256) (the neutral element is still 1).

The algorithm chosen for EDC in QR codes is the Reed-Solomon error correction, which is widely used for streaming data (e.g. CDs, wireless communications) because it allows to correct errors found in bursts, rather than single isolated cases. I won't go into details, but we're stuck with this kind of odd arithmetic.

Operations on GF(256)

The "addition" (XOR'ing) is quite simple. The neutral element with relation to XOR is still 0, as a ^ 0 = a. Also every element is the opposite of itself, since a ^ a = 0.

And since "subtraction" is defined as adding the opposite of the second term, this also means that the "subtraction" is equivalent of the "addition"! In fact: a - b = a ^ (-b) = a ^ b.

Now, about the multiplication. A Galois Field is cyclic, meaning that every non-zero element can be expressed as the power of a "primitive element" α. So, in GF(256), if a = αⁿ and b = α^m, then a ⋅ b = αⁿ ⋅ α^m = α^{n + m}.

But, as we said, a Galois Field is cyclic, so α²⁵⁶ = α. This means that we can take the exponent n + m modulo 255, so we can simplify our computations a bit. In the end, a ⋅ b = α^{(n + m) % 255} (if both a and b are non-zero; the result is of course 0 otherwise).

This also means that for every a, a²⁵⁶ = a, and then a²⁵⁵ = 1, therefore a²⁵⁴ = a^-1, i.e. is the inverse of a. So now we have a way to do divisions: a / b = αⁿ / α^m = αⁿ(α^m)²⁵⁴ = α^{(n + m * 254) % 255}.

Operations in code

XOR'ing is no sweat for JavaScript or any other capable language, but multiplication is another story. The easiest thing to do is creating logarithmic and exponential tables, so it'll be easy to convert a number from and to its exponential notation.

But how do we find α? It's not so hard, as there are φ(255) = 192 primitive elements in GF(256), where φ is Euler's totient function. For the sake of simplicity, we can take α = 2.

Since we're dealing with values all below 256, we can use JavaScript's Uint8Arrays, but if you wish you can use just regular arrays:

const LOG = new Uint8Array(256);
const EXP = new Uint8Array(256);
for (let exponent = 1, value = 1; exponent < 256; exponent++) {
  value = value > 127 ? ((value << 1) ^ 285) : value << 1;
  LOG[value] = exponent % 255;
  EXP[exponent % 255] = value;
}

We just start at 1, then double value at each iteration (shifting by 1 to the left). If value goes over 255, we XOR it with 285. Why 285? I won't go into details (if you're curious, you can find them here), as it has something to do with the relation between elements of a Galois field and polynomials, but rest assured that we'll get all 255 non-zero elements like this.

In the end we'll have:

> LOG
< Uint8Array(256) [0, 0, 1, 25, 2, 50, 26, 198, 3, 223, 51, 238, ...]
> EXP
< Uint8Array(256) [1, 2, 4, 8, 16, 32, 64, 128, 29, 58, 116, 232, ...]

Now we can implement the functions for multiplication and division:

function mul(a, b) {
  return a && b ? EXP[(LOG[a] + LOG[b]) % 255] : 0;
}
function div(a, b) {
  return EXP[(LOG[a] + LOG[b] * 254) % 255];
}

But how will that serve us for error correction? Let's see...

Polynomials in GF(256)

Yes, the Reed-Solomon algorithm uses polynomials! You've probably seen them since high school, and have this form:

a_nxⁿ + a_{n - 1}x^{n - 1} + ... + a₁x + a₀

where a₀, ..., a_n are the coefficients, while x is the variable. You've probably seen (and solved, in the form of equations) them in the field of real numbers, with either real or complex solutions.

But coefficients, exponents and variables could be meant to be in any other field (ring would be enough, actually), even GF(256), inheriting its operations too. So, the "addition" is GF(256)'s addition, i.e. XOR, while multiplication is the one seen above. Exponentiation is just repeated multiplication by itself, as usual.

The good news here is that, as long as our concern is just generation, we do not need to solve any equation!

Polynomial multiplication

Addition is a commutative operation, meaning that a + b = b + a. It is in GF(256) too, because a ^ b = b ^ a. And multiplication is too, but it's also distributive over the addition, meaning that a(b + c) = ab + ac. And this holds in GF(256) too.

This basically means that we can multiply polynomials between them like we used to do with polynomials on real numbers. Suppose we have

p₁(x) = a_nxⁿ + a_{n - 1}x^{n - 1} + ... + a₁x + a₀
p₂(x) = b_mx^m + b_{m - 1}x^{m - 1} + ... + b₁x + b₀

Take the first term of p₁(x), i.e. a_nxⁿ, then multiply it with all the terms of p₂(x):

a_nxⁿ⋅p₂(x) = a_nb_mx^{n + m} + a_nb_{m - 1}x^{n + m - 1} + … + a_nb₁x^{n + 1} + a_nb₀xⁿ

Then do the same with the second term of p₁(x), then the third, and so on. Finally, sum them all together. If this makes your head spin, let's start with and example: x² + 3‍x + 2 and 2‍x² + x + 7. As we've said above, we have to do the following:

(x² + 3‍x + 2)(2‍x² + x + 7)
= x²(2‍x² + x + 7) + 3‍x(2‍x² + x + 7) + 2(2‍x² + x + 7)
= 2‍x⁴ + x³ + 7‍x² + 6‍x³ + 3‍x² + 21‍x + 4‍x² + 2‍x + 14
= 2‍x⁴ + (6 + 1)x³ + (7 + 3 + 4)x² + (21 + 2)x + 14
= 2‍x⁴ + 7‍x³ + 14‍x² + 23‍x + 14

We end up with a polynomial with 5 terms, which is the sum of the amount of terms of both polynomials, minus 1.

In code

We can represent a polynomial with the array of its coefficients, so that x² + 3‍x + 2 could be translated to [1, 3, 2]. Again, since the coefficients can't go over 255, we can use Uint8Array to optimize performances.

Of course all the operations are meant to be done in GF(256), so we're using XOR for addition and the mul function defined above.

Please read the comments in the code snippet below carefully 😁

function polyMul(poly1, poly2) {
  // This is going to be the product polynomial, that we pre-allocate.
  // We know it's going to be `poly1.length + poly2.length - 1` long.
  const coeffs = new Uint8Array(poly1.length + poly2.length - 1);

  // Instead of executing all the steps in the example, we can jump to
  // computing the coefficients of the result
  for (let index = 0; index < coeffs.length; index++) {
    let coeff = 0;
    for (let p1index = 0; p1index <= index; p1index++) {
      const p2index = index - p1index;
      // We *should* do better here, as `p1index` and `p2index` could
      // be out of range, but `mul` defined above will handle that case.
      // Just beware of that when implementing in other languages.
      coeff ^= mul(poly1[p1index], poly2[p2index]);
    }
    coeffs[index] = coeff;
  }
  return coeffs;
}

Polynomial divisions

Ooooh boy. Remember long divisions in high school? Same thing here. (Except we'll just need the rest of the division, not the quotient, but let's save that for later.)

Let't take a dividend polynomial 4‍x³ + 4‍x² + 7‍x + 5, and a divisor polynomial 2‍x + 1. Basically these are the steps:

1. divide the first term of the dividend polynomial (4‍x³) with the first term of the divisor (2‍x, and get 2‍x²);
2. multiply the divisor polynomial by the above quotient (you'll get 4‍x³ + 2‍x²);
3. get the rest by subtracting the result from the dividend (you'll get 2‍x² + 7‍x + 5);
4. if the degree of the rest is lower than the degree of the divisor, you're done; otherwise, the rest becomes your new dividend and you go back to step 1.

For the division above (in the field of real numbers), you'll get a polynomial quotient of 2‍x² + x + 3, and a rest of 2. Now let's do this in JavaScript, and in GF(256).

In code

The quotient polynomial will always be long the difference in length of the dividend and the divisor, plus one.

But it turns out that we don't need the quotient for the Reed-Solomon error correction algorithm, just the rest. So we're defining a function that returns only the rest of the division. The size of the quotient is needed just to count the steps to do.

The code below should be self-explanatory given the example above (it really just does the steps above), but if it's not feel free to ask in the comments:

function polyRest(dividend, divisor) {
  const quotientLength = dividend.length - divisor.length + 1;
  // Let's just say that the dividend is the rest right away
  let rest = new Uint8Array(dividend);
  for (let count = 0; count < quotientLength; count++) {
    // If the first term is 0, we can just skip this iteration
    if (rest[0]) {
      const factor = div(rest[0], divisor[0]);
      const subtr = new Uint8Array(rest.length);
      subtr.set(polyMul(divisor, [factor]), 0);
      rest = rest.map((value, index) => value ^ subtr[index]).slice(1);
    } else {
      rest = rest.slice(1);
    }
  }
  return rest;
}

Now what?

Theory says that a Reed-Solomon error correction data sequence spanning n codewords allows to recover up to n/2 unreadable codewords, they being among the data sequence or in error correction sequence itself (!). Kinda cool, is it?

Remember the error correction table from the first part?

Those percentages are not results, but rather goals: for example, we want the quartile level of correction to be able to recover 25% (a quarter) of the codewords. This means that for this level of correction, there must be as many error correction codewords as data codewords.

For example, a version 2 QR code contains 44 codewords in total. We want to recover up to 11 (25%) of them, which means that we must reserve 22 codewords for EDC. If it looks expensive, it's because it is... but necessary if we want our QR codes to be readable even when damaged.

(The above applies for smaller QR codes. For larger ones, data is often split into two groups, and each group into several blocks - up to 67. Each block has its own error correction sequence, but while data blocks for the second group are always one codeword larger then the blocks from the first group, error correction sequences are all long the same and sized for the larger block, so even for quartile level EDC sequences could be slighly more in total codewords than data. We'll discuss about spliting data into blocks later in the series.)

From this, it's also clear we can't do much better than level H of error correction. If, for example, we wanted 18 codewords to be recoverable out of 44, then we had to use 36 codewords just for error correction, leaving just 8 codewords for data - i.e. less than 18! It's clear it makes little sense, as we'd be better off just repeating the data.

Now let's focus on how to get those error correction codewords out of our data.

Working with (big) polynomials

In the second part, we've sequenced our data (the string https://www.qrcode.com/) into an array of bytes (or codewords, in QR code jargon). Now we've treated polynomials as arrays of values between 0 and 255, so basically using Uint8Arrays for both of them. And that's handy, since for error correction we have to view our data as a polynomial with the codewords as coefficients. Perfect!

Basically, we have our data that becomes this polynomial, called the message polynomial:

65‍x²⁷ + 118‍x²⁶ + 135‍x²⁵ + 71‍x²⁴ + … + 17‍x + 236

But we have 44 total codewords in our version 2 QR code, so we have to multiply this by x to the power of the error correction codewords, i.e. 16. In the end we have:

65‍x⁴³ + 118‍x⁴² + 135‍x⁴¹ + 71‍x⁴⁰ + … + 17‍x¹⁷ + 236‍x¹⁶

Now that we have our big polynomial, we have to divide it by... something, and take the rest of this division: the coefficients of the rest polynomial are going to be our error correction codewords!

But what's this divisor polynomial? Also called…

The generator polynomial

If we have to fill n codewords with error correction data, we need the generator polynomial to be of degree n, so that the rest is of degree n - 1 and so the coefficients are exactly n. What we're going to compute is a polynomial like this:

(x - α⁰)(x - α¹)(x - α²)…(x - α^{n - 2})(x - α^{n - 1})

Now, as we've said, in GF(256) subtraction is the same as addition, and we've also chosen α to be 2. Finally, there are 16 codewords for medium correction in a version 2 QR code, so our generator polynomial is this one:

(x + 1)(x + 2)(x + 4)(x + 8)(x + 16)(x + 32)(x + 64)(x + 128)(x + 29)(x + 58)(x + 116)(x + 232)(x + 205)(x + 135)(x + 19)(x + 38)

The values in the factors are basically the ones from the EXP table computed before. Anyway, let's get our polyMul function rolling!

function getGeneratorPoly(degree) {
  let lastPoly = new Uint8Array([1]);
  for (let index = 0; index < degree; index++) {
    lastPoly = polyMul(lastPoly, new Uint8Array([1, EXP[index]]));
  }
  return lastPoly;
}

Normally, you'd want to pre-compute or cache these polynomials instead of generating them each time. Anyway, our polynomial will be this one:

getGeneratorPoly(16);
// Uint8Array(17) [1, 59, 13, 104, 189, 68, 209, 30, 8, 163, 65, 41, 229, 98, 50, 36, 59]

Finally, we're getting our EDC codewords, by dividing our message polynomial with the generator polynomial:

function getEDC(data, codewords) {
  const degree = codewords - data.length;
  const messagePoly = new Uint8Array(codewords);
  messagePoly.set(data, 0);
  return polyRest(messagePoly, getGeneratorPoly(degree));
}

In the end:

const data = getByteData('https://www.qrcode.com/', 8, 28);
getEDC(data, 44);
// Uint8Array(16) [52, 61, 242, 187, 29, 7, 216, 249, 103, 87, 95, 69, 188, 134, 57, 20]

And we're done! 🙌 It's been a long, but fundamental chapter.

… at least for now. Because much still has to be done in order to create a working QR code.

Stay tuned for the next part, which will be a shorter one. We'll define some details around error correction, and learn how to actually displace all the codewords in the grid. In the following part, we'll talk about masking.