Image process with spatial periodicity measure

1. A method of controlling an image manipulation process in a processor, the method comprising the steps of:

forming a spatial periodicity measure for at least part of an image; and

controlling an image manipulation process in dependence on said spatial periodicity measure;

wherein the step of forming a spatial periodicity measure comprises:

measuring the sparseness of the two-dimensional spatial frequency spectrum of the image on a scale of zero to unity in which a two-dimensional spatial frequency spectrum having all equal values has a sparseness of zero and in which a two-dimensional spatial frequency spectrum having only one non-zero value has a sparseness of unity; and

deriving a spatial periodicity measure data that is a function of said sparseness of the two-dimensional spatial frequency spectrum.

2. A method according to claim 1 in which at least one two-dimensional block of image data is processed in a two-dimensional spatial to frequency domain transform to form transformed image data.

3. A method according to claim 2 in which the said sparseness is a function of the mean-square value of the transformed image data for the said two-dimensional block and the mean value of the transformed image data for that two-dimensional block.

4. A method according to claim 3 in which the sparseness is a linear function of the mean-square value of the transformed image data and the square of the mean value of the transformed image data.

5. A method according to claim 3 in which the sparseness further depends on the mean-square value of one or more transform output values corresponding to low spatial frequencies.

6. A method according to claim 2 in which the said spatial periodicity measure is normalized by division by a function of the mean-square transformed image data for the said two-dimensional block.

7. A method according to claim 1 in which said sparseness is measured by counting the number of spatial frequency values in the two-dimensional spatial frequency spectrum that exceed a threshold value.

8. A method according to claim 7 in which said threshold comprises a representative value for the spatial frequency values in the two-dimensional spatial frequency spectrum.

9. A method according to claim 1 in which measuring the sparseness of the two-dimensional spatial frequency spectrum of the image utilizes a technique selected from the group of: allocating values of the spectrum to frequency bins and counting the number of bins that contain non-zero values; comparing values of the spectrum with a threshold and counting the number of values that exceed the threshold; comparing values of the spectrum with a representative value for the spectrum and counting the number of values that exceed the representative value; and forming a function of the mean-square value of the spectrum values and the mean value of the spectrum values.

10. A method according to claim 1 in which the said image manipulation process comprises the interpolation of at least one new image within a sequence of images.

11. A method according to claim 10 , in which said interpolation comprises motion compensated interpolation and in which said motion compensation is controlled in dependence on said spatial periodicity measure.

12. An image interpolation apparatus comprising a motion compensated interpolator and a spatial periodicity measurement block, wherein:

the spatial periodicity measurement block serves to measure the sparseness of the two-dimensional spatial frequency spectrum of the image on a scale of zero to unity in which a two-dimensional spatial frequency spectrum having all equal values has a sparseness of zero and in which a two-dimensional spatial frequency spectrum having only one non-zero value has a sparseness of unity; and to derive a spatial periodicity measure data that is a function of the said sparseness of the two-dimensional spatial frequency spectrum; and the motion compensated interpolator is controlled in dependence upon said spatial periodicity.