Method and apparatus for adaptive weight selection for motion compensated prediction

1. An apparatus, comprising:

an encoder for encoding a single prediction picture by deriving a set of weighting parameters, selecting a single weighting parameter in the set based upon a selection criteria, and applying the selected single weighting parameter to a single reference picture used to encode the single prediction picture,

wherein said encoder selects the single weighting parameter in the set based upon a full encoding of the picture with each of the weighting parameters in the set and using rate distortion optimization, and

wherein the weighting parameters in the set correspond to different weighting methods, and only one weighting parameter is derived for each of the different weighting methods.

2. The apparatus of claim 1 , wherein the rate distortion optimization is performed using Lagrangian multipliers.

3. The apparatus of claim 1 , wherein said encoder selects the single weighting parameter based upon a computation of a distortion metric.

4. The apparatus of claim 3 , wherein the distortion metric is computed as an absolute picture difference between the single prediction picture and a weighted reference picture.

5. The apparatus of claim 3 , wherein the distortion metric is computed as a difference between histograms of the single prediction picture and a weighted reference picture.

6. The apparatus of claim 3 , wherein said encoder uses at least one of histogram methods, picture mean averages, linear regression, displaced differences, and iterative methods to estimate explicit weighting parameters for the single prediction picture.

7. The apparatus of claim 1 , wherein said encoder also selects an optimal weighting method from among the different weighting methods to encode the single prediction picture based on distortion characterization, the optimal weighting method selected from among a non-weighted prediction method, an explicit weighted prediction method, and an implicit weighted prediction method, each included in the set of weighting parameters, and wherein the single weighting parameter is explicitly coded in a resultant bitstream in the explicit weighted prediction method and is implicitly derivable from the resultant bitstream in the implicit weighted prediction method.

8. The apparatus of claim 7 , wherein said encoder selects the optimal weighting method to encode the single prediction picture using at least one transition detection method.

9. A video encoding method, comprising:

encoding a single prediction picture by deriving a set of weighting parameters;

selecting a single weighting parameter in the set based upon a selection criteria; and

applying the selected single weighting parameter to a single reference picture used to encode the single prediction picture,

wherein said encoding step selects the single weighting parameter in the set based upon a full encoding of the single prediction picture with each of the weighting parameters in the set and using rate distortion optimization, and P 1 wherein the weighting parameters in the set correspond to different weighting methods, and only one weighting parameter is derived for each of the different weighting methods.

10. The method of claim 9 , wherein the rate distortion optimization is performed using Lagrangian multipliers.

11. The method of claim 9 , wherein said encoding step selects the single weighting parameter based upon a computation of a distortion metric.

12. The method of claim 11 , wherein the distortion metric is computed as an absolute picture difference between the single prediction picture and a weighted reference picture.

13. The method of claim 11 , wherein the distortion metric is computed as a difference between histograms of the single prediction picture and a weighted reference picture.

14. The method of claim 11 , wherein said encoding step uses at least one of histogram methods, picture mean averages, linear regression, displaced differences, and iterative methods to estimate explicit weighting parameters for the single prediction picture.

15. The method of claim 9 , wherein said encoding step also selects an optimal weighting method from among the different weighting methods to encode the single prediction picture based on distortion characterization, the optimal weighting method selected from among a non-weighted prediction method, an explicit weighted prediction method, and an implicit weighted prediction method, each included in the set of weighting parameters, and wherein the single weighting parameter is explicitly coded in a resultant bitstream in the explicit weighted prediction method and is implicitly derivable from the resultant bitstream in the implicit weighted prediction method.

16. The method of claim 15 , wherein said encoding step selects the optimal weighting method to encode the single prediction picture using at least one transition detection method.

17. The apparatus of claim 7 , wherein different priorities are respectively assigned to the different weighting methods.

18. The apparatus of claim 17 , wherein at least some of the different priorities are altered depending upon whether a transition detection method is unused to select the at least one weighting parameter.

19. The apparatus of claim 18 , wherein the at least some of the different priorities that are altered depending upon whether the transition detection method is unused to select the at least one weighting parameter comprise the non-weighted prediction method and the implicit weighted prediction method.

20. The method of claim 15 , wherein different priorities are respectively assigned to the different weighting methods.

21. The method of claim 20 , wherein at least some of the different priorities are altered depending upon whether a transition detection method is unused to select the at least one weighting parameter.

22. The method of claim 21 , wherein the at least some of the different priorities that are altered depending upon whether the transition detection method is unused to select the at least one weighting parameter comprise the non-weighted prediction method and the implicit weighted prediction method.