GameDev.netBitmap Rotation

Bitmap Rotation
by Mike Morton
Not long ago, I asked for ideas on how to quickly rotate a bitmapped image by an arbitrary angle. Quite a response! Thanks to all, including several whose messages don't appear here because they were subsets of other messages I got. First, for those of you who prefer the library, here's a summary of the references I got. Following are some of the responses, several of which are quite detailed.
*Fant, K.M.*- (1986) IEEE Computer Graphics & Applications 6,1 71-80. "A nonaliasing, real-time spatial transform technique."
*Foley & Van Dam*- "Fundamentals of Interactive Computer Graphics" chapter 8
*Jackson, T.*- (1987) Graphics and Image Processing Algorithms for the miniDAP. BSc. Project Report, Dept. Computer SCience & Statistics, Queen Mary College, London University, Mile End Rd., London.
*Alan Paeth*- "A Fast Algorithm for General Raster Rotation," Graphics Interface '86, pp. 77-81, May 1986.
*A. Tanaka, M. Kameyama, S. Kazama, and O. Watanabe*- "A Rotation Method for Raster Image Using Skew Transformation," Proc. IEEE Conference on Computer Vision and Pattern Recognition, pp. 272-277, June 1986.
From: David Fox The idea is that you compose the functions "shear" and "transpose". "Shear" takes a bitmap like this
and shears it by a given angle x to give this
Transpose rotates that about the diagonal to give this
Then a shear in the other direction gives this
Finally, another transpose yields the rotated bitmap
I did not invent this algorithm, so please don't attribute it to me. Let me know what kind of replies you get, I'm curious whether this algorithm is widely known (and whose it is), or unknown, or whether there are other algorithms to do this. Enjoy. David Fox
From: jeff@lorrie.atmos.washington.edu (Jeff Bowden) See Foley & Van Dam "Fundamentals of Interactive Computer Graphics" chapter 8 (I think). For a 2D rotate about the origin, multiply all coordinates by this matrix:
NOTE: I have no actual experience with these methods on bitmaps. I only use the coordinate transformation matrix on lines and triangles. There may be problems with bresenham not generating the same number of points for the rotated line as the horizontal source line. If there is a difference it should be only 1 or 2 pixels, you need to study its behaviour before you will know. I contemplated implementing method 3 at one point and actually coded some of it before I got sidetracked with other things. No, I don't have the code anywhere anymore. From: oster%SOE.Berkeley.EDU@jade.berkeley.edu (David Phillip Oster) For gray scale images, of course, this reduces to a simple interpolation problem: each destination pixel is the weighted sum of some small neighborhood of source pixels. You need to compute the weights, and come up with an efficient algorithm for marching through the bitmap. From: mcvax!cwi.nl!edwin@uunet.UU.NET (Edwin Blake)
In reply to your question I have made the following summary (its still too long!). If you would like more implementation details please contact me. What I did not mention is that there is a three-pass method (suited to SIMD machines) which Jackson [see below] develops but this is probably irrelevant for you as well. -=-=-=-=-=-=-=-=-=-=-=-=-=- 8th November, 1988 E.H.Blake
The general linear transformations of images (translations, rotations shear etc) can be achieved by a vector addition, and multiplication by a 2x2 matrix. Clearly we wish to separate out translations of images since they are by far the easiest and cheapest transformation. We are now left with the transformations represented by the same two-dimensional matrix applied to the coordinates of each point in the image. It might be convenient to separate this into components which can be dealt with in different ways: i.e. change in the size in the x and y directions, shear in those directions and pure rotation. Since we are going to deal with images on a raster questions about re-sampling and aliasing will arise. We shall present two fundamentally different approaches. The one approach deals with fractional pixels and attempts to minimise sampling errors by distributing parts of a source pixel amongst the destination pixels. The other approach deals only with whole pixels and reduces temporal aliasing (i.e. flicker) at the expense of spatial resolution. When shearing images the whole pixel method produces pixels might not be quite right for their position but no pixels are lost or duplicated. The whole pixel approach can also be used for overall size changes in the images. When the size of the image changes we either cull or duplicate certain pixels. Particularly with size reduction this can lead to unacceptable results because certain features in the image may disappear. This leads us to adopt the fractional pixel resampling approach. There is another important difference between the two approaches however: the fractional pixel approach has to examine and interpret pixel values and must be able to combine them. The whole pixel approach for shearing combined with a duplicate/cull method of size change is independent of the values of the pixels. This value independence (which is really a type independence) is important where the pixel values are themselves isolated points in a much bigger colour space. For example, if the pixels are pointers into a colour table then we cannot in general expect to be able to average the two pointers and come up with a pointer to the intermediate colour. These simple operations repeated over a large number of pixels make them ideal candidates for special purpose VLSI or SIMD machines. In fact the routines described below are partly inspired by algorithms originally intended for the miniDAP (a 32x32 SIMD machine) [Jackson, 1987] or for a special purpose processor [Fant, 1986].
The 2x2 transformation matrix can be written as a composition of transformations in a number of ways. The decomposition used depends on the order in which the transformations are applied. The basic image transformation which leaves its source unchanged is the unit matrix. If we first shear in the y direction, then in the x direction and finally scale the image the decomposition is as follows: | a b | = | m 0 | * | 1 x | * | 1 0 | (1) | c d | | 0 n | | 0 1 | | y 1 | = | m + mxy mx | | ny n | In the decomposition y is a shear parallel to the y-axis, x is a shear parallel to the x-axis, n represents a scaling in the y dimension and m a scaling in the x dimension. By identifying terms we can see that: n = d and provided d != 0 y = c/d & m = a - bc/d (2) and provided ad - bc is non-zero x = db/(ad - bc) If the determinant, ad - bc, is zero then the transformation is singular. This means that the image need not be treated as a plane. If the transformation is non-singular but d = 0, then we can rotate the image by ninety degrees and exchange -b for d. If on the other hand we first apply all the y-direction changes (shear and scaling) and then the x-direction changes, the decomposition is as follows: | a b | = | e f | * | 1 0 | (3) | c d | | 0 1 | | g h | = | e + fg fh | | g h | In the decomposition g is a shear parallel to the y-axis and h the scaling in that direction, f is a shear parallel to the x-axis and e the corresponding scaling. Then: h = d g = d (4) and provided d != 0 f = b/d e = bc/(a - d) Similar considerations as in (2) regarding d apply to (4).
In order to refer to this method of image transformation easily it will be called the "Quick" method. The quick method always moves whole pixels without interpretation, it is thus independent of the underlying type of the representation. There are four basic kinds of operation we shall need: - Pixel shear (translation) according to position.
- Shearing of pixels in two dimensions together, without writing out the intermediate results.
- Selective pixel duplication to achieve fractional size increases.
- Selective pixel culling to achieve fractional decreases in image size.
A common theme to all these transformations is distributing a fraction of an integral set of operations over an interval. For example, choosing which pixels to duplicate to get 10% size increase, or which lines to shift for a fractional shear. This has a very efficient solution in Bresenham's well known algorithm, which is mainly used for line drawing [see any textbook, e.g. Newman & Sproull]. The other problem is combining two shears in two-dimensions. The simplest way would be to write out the intermediate image and then transform it a second time. However, by tracing the jagged path which an output line follows we can derive a scanline algorithm which produces the sheared image. By duplicating or neglecting selected pixels and then duplicating or neglecting selected lines we have an efficient and general image transformation method. The algorithm notionally employed the decomposition of equation 2 but without writing out any intermediate results. This algorithm was fully implemented and its combination of features may very well be unique.
This image transformation writes out its intermediate results. The pixel values are interpreted, which means that colour errors can occur from resampling. For grey level images however the output is much better, because anti-aliased, than the previous method. For brevity it will be referred to as the "Perfect" method. The algorithm interpolates a selected window of input pixels to produce the desired view of output pixels. The columns of the image are first processed and then the rows of the resulting intermediate image. Because of the two pass nature of the algorithm shears can be done easily for each dimension separately. The "perfect" method is well suited to VLSI implementations [Fant, 1986], and the reader is referred to Fant's article for more details. The decomposition derived in equations 3 and 4 apply to this algorithm. Using the decomposition into the shear and scaling components avoids the rather laborious procedure used by Fant which involved calculating the positions of the four corners of an image.
Because the two pass algorithm was so slow there was an attempt to produce an intermediate algorithm. This algorithm is essentially the same as "Quick" except that pixels are not culled when the image decreases in size. Instead a simple averaging is performed. Successive pixels which would be discarded are accumulated and averaged with the normal output pixel of a location. The algorithm was only used for images which decreased in size. This was the most common bad case for the "Quick" method. Whether this method is worth the extra code is not clear. From: Alan Wm Paeth I presented a paper on this in Graphics Interface '86. The code can run on a framebuffer (we have an Adage/Ikonas) or a mainframe (Vax implementation). Three passes over the data are made; each "shear" the data without scaling. This allows simple resampling and all integer math (as opposed to the previously known 2-pass varients). It also is good to ninety degrees (the two pass version breaks down rapidly after 45). In the 90 deg case, the rotate becomes a simple "shuffle", as done on the "Blit". I can send you the source code or the "troff" text of the paper. [Alan said in a later note that this offer is for anyone, not just me. - MM] Computer Graphics Laboratory University of Waterloo Enjoy... -- Mike Morton // P.O. Box 11378, Honolulu, HI 96828, (808) 676-6966 HST
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