Another fast fixed-point sine approximation

Gaddammit!

 

So here I am, looking forward to a nice quiet weekend; hang back, watch some telly and maybe read a bit – but NNnnneeeEEEEEUUUuuuuuuuu!! Someone had to write an interesting article about sine approximation. With a challenge at the end. And using an inefficient kind of approximation. And so now, instead of just relaxing, I have to spend my entire weekend and most of the week figuring out a better way of doing it. I hate it when this happens >_<.

 

Okay, maybe not.

 

Sarcasm aside, it is an interesting read. While the standard way of calculating a sine – via a look-up table – works and works well, there's just something unsatisfying about it. The LUT-based approach is just … dull. Uninspired. Cowardly. Inelegant. In contrast, finding a suitable algorithm for it requires effort and a modicum of creativity, so something like that always piques my interest.

In this case it's sine approximation. I'd been wondering about that when I did my arctan article, but figured it would require too many terms to really be worth the effort. But looking at Mr Schraut's post (whose site you should be visiting from time to time too; there's good stuff there) it seems you can get a decent version quite rapidly. The article centers around the work found at devmaster thread 5784, which derived the following two equations:

(1) \begin{eqnarray} S_2(x) &=& \frac4\pi x - \frac4{\pi^2} x^2 \\ \\ S_{4d}(x) &=& (1-P)S_2(x) + P S_2^2(x) \end{eqnarray}

These approximations work quite well, but I feel that it actually uses the wrong starting point. There are alternative approximations that give more accurate results at nearly no extra cost in complexity. In this post, I'll derive higher-order alternatives for both. In passing, I'll also talk about a few of the tools that can help analyse functions and, of course, provide some source code and do some comparisons.

Continue reading

mode 7 addendum

Okay. Apparently, I am an idiot who can't do math.

 

One of the longer chapters in Tonc is Mode 7 part 2, which covers pretty much all the hairy details of producing mode 7 effects on the GBA. The money shot for in terms of code is the following functions, which calculates the affine parameters of the background for each scanline in section 21.7.3.

IWRAM_CODE void m7_prep_affines(M7_LEVEL *level)
{
    if(level->horizon >= SCREEN_HEIGHT)
        return;

    int ii, ii0= (level->horizon>=0 ? level->horizon : 0);

    M7_CAM *cam= level->camera;
    FIXED xc= cam->pos.x, yc= cam->pos.y, zc=cam->pos.z;

    BG_AFFINE *bga= &level->bgaff[ii0];

    FIXED yb, zb;           // b' = Rx(theta) *  (L, ys, -D)
    FIXED cf, sf, ct, st;   // sines and cosines
    FIXED lam, lcf, lsf;    // scale and scaled (co)sine(phi)
    cf= cam->u.x;      sf= cam->u.z;
    ct= cam->v.y;      st= cam->w.y;
    for(ii= ii0; ii<SCREEN_HEIGHT; ii++)
    {
        yb= (ii-M7_TOP)*ct + M7_D*st;
        lam= DivSafe( yc<<12,  yb);     // .12f    <- OI!!!

        lcf= lam*cf>>8;                 // .12f
        lsf= lam*sf>>8;                 // .12f

        bga->pa= lcf>>4;                // .8f
        bga->pc= lsf>>4;                // .8f

        // lambda·Rx·b
        zb= (ii-M7_TOP)*st - M7_D*ct;   // .8f
        bga->dx= xc + (lcf>>4)*M7_LEFT - (lsf*zb>>12);  // .8f
        bga->dy= zc + (lsf>>4)*M7_LEFT + (lcf*zb>>12);  // .8f

        // hack that I need for fog. pb and pd are unused anyway
        bga->pb= lam;
        bga++;
    }
    level->bgaff[SCREEN_HEIGHT]= level->bgaff[0];
}

For details on what all the terms mean, go the page in question. For now, just note that call to DivSafe() to calculate the scaling factor λ and recall that division on the GBA is pretty slow. In Mode 7 part 1, I used a LUT, but here I figured that since the yb term can be anything thanks to the pitch you can't do that. After helping Ruben with his mode 7 demo, it turns out that you can.

 

Fig 1. Sideview of the camera and floor. The camera is tilted slightly down by angle θ.

Fig 1 shows the situation. There is a camera (the black triangle) that is tilted down by pitch angle θ. I've put the origin at the back of the camera because it makes things easier to read. The front of the camera is the projection plane, which is essentially the screen. A ray is cast from the back of the camera on to the floor and this ray intersects the projection plane. The coordinates of this point are xp = (yp, D) in projection plane space, which corresponds to point (yb, zb) in world space. This is simply rotating point xp by θ. The scaling factor is the ratio between the y or z coordinates of the points on the floor and on the projection plane, so that's:

\lambda = y_c / y_b,

and for yb the rotation gives us:

y_b = y_p cos \theta + D sin \theta,

where yc is the camera height, yp is a scanline offset (measured from the center of the screen) and D is the focus length.

Now, the point is that while yb is variable and non-integral when θ ≠ 0, it is still bounded! What's more, you can easily calculate its maximum value, since it's simply the maximum length of xp. Calling this factor R, we get:

R = \sqrt{max(y_p)^2 + D^2}

This factor R, rounded up, is the size of the required LUT. In my particular case, I've used yp= scanline−80 and D = 256, which gives R = sqrt((160−80)² + 256²) = 268.2. In other words, I need a division LUT with 269 entries. Using .16 fixed point numbers for this LUT, the replacement code is essentially:

// The new division LUT. For 1/0 and 1/1, 0xFFFF is used.
u16 m7_div_lut[270]=
{
    0xFFFF, 0xFFFF, 0x8000, 0x5556, ...
};


// Inside the function
    for(ii= ii0; ii<SCREEN_HEIGHT; ii++)
    {
        yb= (ii-M7_TOP)*ct + M7_D*st;           // .8
        lam= (yc*m7_div_lut[yb>>8])>>12;        // .8*.16/.12 = .12
       
        ... // business as usual
    }

At this point, several questions may arise.

  • What about negative yb? The beauty here is that while yb may be negative in principle, such values would correspond to lines above the horizon and we don't calculate those anyway.
  • Won't non-integral yb cause inaccurate look-ups? True, yb will have a fractional part that is simply cut off during a simple look-up and some sort of interpolation would be better. However, in testing there were no noticeable differences between direct look-up, lerped look-up or using Div(), so the simplest method suffices.
  • Are .16 fixed point numbers enough?. Yes, apparently so.
  • ZOMG OVERFLOW! Are .16 fixed point numbers too high? Technically, yes, there is a risk of overflow when the camera height gets too high. However, at high altitudes the map is going to look like crap anyway due to the low resolution of the screen. Furthermore, the hardware only uses 8.8 fixeds, so scales above 256.0 wouldn't work anyway.

And finally:

  • What do I win? With Div() m7_prep_affines() takes about 51k cycles. With the direct look-up this reduces to about 13k: a speed increase by a factor of 4.
 

So yeah, this is what I should have figured out years ago, but somehow kept overlooking it. I'm not sure if I'll add this whole thing to Tonc's text and code, but I'll at least put up a link to here. Thanks Ruben, for showing me how to do this properly.

On arctangent.

The arctangent is one of the more interesting trigonometry functions – and by “interesting” I of course mean a bitch to get right. I've been meaning to write something about the various methods of calculating it for a while now and finally got round to it recently.

I, ah, uhm, may have gotten a little carried away with it though ... but that's okay,! At least now I while one to recommend.

New doc: matrices from a geometry perspective

Matrices from a geometry perspective” is now out. Vector and matrix math is used heavily in computer graphics because it involved geometry and coordinate transformations. While this is widely known, I've seen many people struggle with the basic concepts of how it's supposed to work. this is especially true in the GBA/NDS community, where erroneous information about the affine matrix is still found in many demos and even library code even today. And that just kills me, because the fundamentals are really quite simple to grasp once you look at it from the right direction(1)

I've wanted to write something on the subject for quite some time. Yeah, there were a few sections in Tonc that covered it, but not in the amount of detail I wanted it. A recent email (thanks Ian) finally spurred me to write something down.

In the document, I describe what points, vectors and coordinates really are in geometrical terms. This may seem obvious, but it's always good to go over the basics again because overlooking those is often the reason for misunderstandings. Then it describes how coordinate transformations work and how matrices fit into the subject.

I've tried to keep the hardcore math to a minimum in order to keep it understandable. I'd really like to get some feedback on this to know if it is comprehensible to people who don't already know how all this stuff works. If there are other items that anyone feels I should add, I'll consider that as well. I should probably also note that many parts of it have already been rewritten three times already, so if sentences don't make sense, let me know so I can fix it.


linky: Matrices from a geometry perspective.


Notes:
  1. I'm not saying it's not easy to make mistakes here (it's still math we're talking about), but the basic concepts are not that complex.