Estimating weighted-prediction parameters

1. A method, comprising:

obtaining first weighted prediction parameters;

obtaining second weighted prediction parameters based on a scaled reference image histogram derived from samples of a reference image and the first weighted prediction parameters; and

responsive to a component of an image, enabling the use of the second weighted prediction parameters for predicting a block of the image based on a first histogram distortion obtained between an image histogram, derived from samples of the image, and a reference image histogram, derived from samples of the reference image, and on a second histogram distortion obtained between the image histogram and a scaled reference image histogram derived from the reference image histogram and the second weighted prediction parameters.

2. The method of claim 1 , wherein obtaining the second weighted prediction parameters depends on a configuration parameter.

3. The method of claim 1 , wherein enabling the use of the second weighted prediction parameters for predicting the block of the image is performed for a first component of the image, the method further comprising, for a second component of the image:

enabling the use of the first weighted prediction parameters for predicting the block of the image based on the first weighted prediction parameters and a sum of absolute differences between samples of the image and samples of a scaled reference image, and a sum of absolute differences between samples of the image and samples of the reference image.

4. The method of claim 1 further comprising:

spreading the image histogram before obtaining the first weighted prediction parameters.

5. The method of claim 4 , wherein spreading the image histogram depends on the bit-depth of samples of the image and a given bit-depth.

6. The method of claim 4 , wherein spreading the image histogram comprises distributing the samples of the image relative to a peak of the image histogram around said peak.

7. The method of claim 1 , wherein the first weighted prediction parameters are obtained either from default weighted parameters or based on samples of the image and samples of a reference image.

8. A non-transitory computer readable medium comprising instructions which, when the instructions are executed by a computer, cause the computer to:

obtain first weighted prediction parameters;

obtain second weighted prediction parameters based on a scaled reference image histogram derived from samples of a reference image and the first weighted prediction parameters; and

responsive to a component of an image, enable the use of the second weighted prediction parameters for predicting a block of the image based on a first histogram distortion obtained between an image histogram, derived from samples of the image, and a reference image histogram, derived from samples of the reference image, and on a second histogram distortion obtained between the image histogram and a scaled reference image histogram derived from the reference image histogram and the second weighted prediction parameters.

9. The non-transitory computer readable medium of claim 8 , wherein obtaining the second weighted prediction parameters depends on a configuration parameter.

10. The non-transitory computer readable medium of claim 8 , wherein to enable the use of the second weighted prediction parameters for predicting the block of the image is performed for a first component of the image, the instructions further cause the computer to, for a second component of the image, enable the use of the weighted prediction parameters for predicting the block of the image based on the first weighted prediction parameters and a sum of absolute differences between samples of the image and samples of a scaled reference image, and a sum of absolute differences between samples of the image and samples of the reference image.

11. The non-transitory computer readable medium of claim 8 , wherein the instructions further cause the computer to spread the image histogram before obtaining the first weighted prediction parameters.

12. The non-transitory computer readable medium of claim 11 , wherein spreading the image histogram depends on the bit-depth of samples of the image and a given bit-depth.

13. The non-transitory computer readable medium of claim 11 , wherein spreading the image histogram comprises distributing the samples of the image relative to a peak of the image histogram around said peak.

14. The non-transitory computer readable medium of claim 8 , wherein the first weighted prediction parameters are obtained either from default weighted parameters or based on samples of the image and samples of a reference image.

15. An apparatus, comprising one or more processors configured for:

obtaining first weighted prediction parameters;

obtaining second weighted prediction parameters based on a scaled reference image histogram derived from samples of a reference image and the first weighted prediction parameters; and

responsive to a component of an image, enabling the use of the second weighted prediction parameters for predicting a block of the image based on a first histogram distortion obtained between an image histogram, derived from samples of the image, and a reference image histogram, derived from samples of the reference image, and on a second histogram distortion obtained between the image histogram and a scaled reference image histogram derived from the reference image histogram and the second weighted prediction parameters.

16. The apparatus of claim 15 , wherein obtaining the second weighted prediction parameters depends on a configuration parameter.

17. The apparatus of claim 15 , wherein enabling the use of the second weighted prediction parameters for predicting the block of the image is performed for a first component of the image, the one or more processors being further configured for, for a second component of the image:

enabling the use of the first weighted prediction parameters for predicting the block of the image based on the first weighted prediction parameters and a sum of absolute differences between samples of the image and samples of a scaled reference image, and a sum of absolute differences between samples of the image and samples of the reference image.

18. The apparatus of claim 15 , the one or more processors being further configured for spreading the image histogram before obtaining the first weighted prediction parameters.