Method of providing a sharpness measure for an image

1. An image processing device arranged to, and comprising the steps of:

detect an object region within an acquired image;

obtain meta-data for the acquired image;

scale the chosen object region to a fixed size;

calculate a gradient map for the scaled object region;

compare the gradient map against a threshold, wherein the threshold for the acquired image is a function of at least: a contrast level for the detected object region, a distance to the subject and an ISO value used for image acquisition;

determine a sharpness measure for the object region as a function of the filtered gradient map values, the sharpness measure being proportional to the filtered gradient map values; and

responsive to the sharpness measure indicating the object region is not sufficiently focussed, change focusing for acquirinq a subsequent image.

2. An image processing device according to claim 1 wherein the fixed size is 200×100 pixels.

3. An image processing device according to claim 1 wherein the object is a face.

4. An image processing device according to claim 3 wherein the method further arranged to crop the face region to only use a face region containing at least one eye as the chosen object region.

5. An image processing device according to claim 1 further arranged to calculate a noise measure for the object region by:

splitting the filtered gradient map into an array;

determining a mean sharpness for each cell of the array;

sorting the mean sharpness values in order of magnitude;

selecting a sub-range of sharpness values from the sorted sharpness values; and

calculating said noise measure for the object region as a function of said sub-range of sharpness values.

6. An image processing device according to claim 5 wherein said sub-range comprises a group of the lowest valued sharpness values.

7. An image processing device according to claim 5 wherein the sharpness measure is a function of the noise measure for the object region, the sharpness measure being inversely proportional to the noise measure.

8. An image processing device according to claim 1 further arranged to calculate a threshold measure for the object region by:

splitting the filtered gradient map into an array;

determining a mean sharpness for each cell of the array;

sorting the mean sharpness values in order of magnitude;

the threshold for the object region, thr_Face, being defined as follows:

thr_Face =mean(sorted_vals(high))* w b +mean(sorted_vals(low))* w r ;

where:

sorted_vals(low) and sorted_vals(high) are groups of lowest and highest mean sharpness values respectively;

w b αf(distance to subject) −(ISO value / contrast level * d);

w r =1 −w b ; and

d is a constant.

9. An image processing device according to claim 8 wherein the sharpness measure is proportional to the threshold measure for the object region.

10. An image processing device according to claim 8 wherein the threshold for the acquired image is proportional to sum of thresholds for ISO, contrast and distance adjusted by said threshold for the object region, thr_Face.

11. An image processing device according to claim 1 wherein the threshold tor the acquired Image is the maximum of: a constant; and a sum of thresholds for ISO, contrast and distance.

12. An image processing device according to claim 1 wherein the threshold for the acquired image is proportional to average luminance, ISO and distance.

13. An image processing device according to claim 1 further arranged to extract meta-data from the acquired image to determine parameters for each of said ISO, contrast and distance thresholds.