FastMaskedNoiseDemo([numRects=1][, rectSize=128][, scale=1])

Demonstrates how to generate and draw noise patches on-the-fly in a fast
way. The patches are shown through circular apertures by the use of

numRects = Number of random patches to generate and draw per frame.

rectSize = Size of the generated random noise image: rectSize by rectSize
pixels. This is also the size of the Psychtoolbox noise

scale = Scalefactor to apply to texture during drawing: E.g. if you’d set
scale = 2, then each noise pixel would be replicated to draw an image
that is twice the width and height of the input noise image. In this
demo, a nearest neighbour filter is applied, i.e., pixels are just
replicated, not bilinearly filtered – Important to preserve statistical
independence of the random pixel values!

If you play around with the parameters and compare performance to the
FastNoiseDemo, you will notice the following:

  • Scaling the stimulus to a bigger size is nearly free on modern graphics
    hardware, so you can generate low-resolution noise stimuli that still
    fill a huge fraction of your display area if you want.

  • Drawing the aperture is nearly free, i.e., this demo runs nearly as
    fast as the FastNoiseDemo without masking. This is because modern
    gfx-hardware is highly optimized for texture drawing and alpha blending.
    The aperture textures are cached in fast onboard VRAM memory to speed up
    drawing them.

  • The drawing speed is mostly limited by how fast Matlab can compute new
    random dot number matrices, not by properties of the stimulus images.

Path   Retrieve current version from GitHub | View changelog