Benchmark each interpolation function for RGBA and YCbCr images.
Reduce the image size used when benchmarking to reduce the influence of
memory performance on results.

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This fix doesn't currently work concurrently. This will be fixed in another commit.

Increased performance by 30% for RGBA and 45% for Gray images, minor
performance increase for 16-bit images.

The start offset calculated by createWeights are stored in a slice and
passed to the resize functions to prevent duplication of effort.

This is a complete rewrite! The tight scaling loop needs data locality for optimal performance. The old version used lots of pointer redirections to access image data which was bad for data locality. By providing the complete loop for each image type, this problem is solved. Unfortunately this increases code duplication but the result should be worth it: I could measure a ~6x speed-up for certain test cases!

Caching kernel weights makes using the LUT based approach obsolete. Now they seem to be even slower than their counterparts.

For each row the convolution kernel is evaluated at fixed positions around "u". By calculating these values once for each row, a huge speedup is achieved.

Run "go test -bench LargeJpegThumb" to benchmark all functions at once.

Should be ~5x faster! More optimization will follow.

before:
> go test -bench .
PASS
Benchmark_BigResizeLanczos3-4	       1	2438137093 ns/op
Benchmark_BigResizeLanczos3Lut-4	       1	1157612362 ns/op
Benchmark_Reduction-4	       2	 743950618 ns/op

after:
> go test -bench .
PASS
Benchmark_BigResizeLanczos3-4	       5	 403685685 ns/op
Benchmark_BigResizeLanczos3Lut-4	      10	 225539497 ns/op
Benchmark_Reduction-4	      10	 207004759 ns/op