Image process with spatial periodicity measure

1. A non-transitory computer-readable medium including instructions configured to cause a computer system to implement a 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. The non-transitory computer-readable medium of 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. The non-transitory computer-readable medium of 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.

4. The non-transitory computer-readable medium of 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.

5. The non-transitory computer-readable medium of claim 4 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.

6. The non-transitory computer-readable medium of claim 4 in which the sparseness further depends on the mean-square value of one or more transform output values corresponding to low spatial frequencies.

7. The non-transitory computer-readable medium of 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. The non-transitory computer-readable medium of claim 7 in which said threshold comprises a representative value for the spatial frequency values in the two-dimensional spatial frequency spectrum.

9. The non-transitory computer-readable medium of 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. The non-transitory computer-readable medium of claim 1 in which the said image manipulation process comprises the interpolation of at least one new image within a sequence of images.

11. The non-transitory computer-readable medium of 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. A non-transitory computer-readable medium including instructions configured to cause a computer system to implement a motion compensated image manipulation process comprising the steps of:

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

controlling motion compensation of said motion compensated 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 utilizing 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; and

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

13. The non-transitory computer-readable medium of claim 12 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.

14. The non-transitory computer-readable medium of claim 13 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.

15. The non-transitory computer-readable medium of claim 12 in which the said image manipulation process comprises the interpolation of at least one new image within a sequence of images.

16. The non-transitory computer-readable medium of claim 12 where said measure of the sparseness of the two-dimensional spatial frequency spectrum of the image has 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.

17. A method of controlling a motion compensated 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 motion compensation of said 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 utilizing 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; and

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

18. The method of claim 17 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.

19. The method of claim 17 in which the said image manipulation process comprises the interpolation of at least one new image within a sequence of images.

20. The method of claim 17 where said measure of the sparseness of the two-dimensional spatial frequency spectrum of the image has 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.