Identifying parameter image adjustments using image variation and sequential processing

1. A computer-implemented method for training a neural network system to identify parameter image adjustments, the method comprising:

identifying a set of candidate parameter values associated with a parameter to be analyzed in association with an image;

rendering the image in accordance with each candidate parameter value to generate a set of rendered images;

generating an analysis vector for each of the rendered images based on corresponding sets of features identified for each of the rendered images;

concatenating the analysis vectors into a composite analysis vector;

using a neural network to identify a parameter image adjustment to apply to the image based on the composite analysis vector; and

training the neural network based on a comparison of the identified parameter image adjustment and a reference parameter value associated with the parameter being analyzed.

2. The computer-implemented method of claim 1 , further comprising:

selecting the parameter to be analyzed based on a predetermined sequence of parameters, a random selection of parameters, an algorithm, or parameter selection neural network.

3. The computer-implemented method of claim 1 , wherein the set of candidate parameter values comprises a set of equally spaced values within an interval, a set of symmetrically spaced values about a nominal value, or randomly spaced values within an interval.

4. The computer-implemented method of claim 1 , wherein the set of candidate parameter values is identified based on a predetermined set of parameter values for the parameter or based on analysis of the image.

5. The computer-implemented method of claim 1 further comprising:

selecting the neural network based on the parameter being analyzed.

6. The computer-implemented method of claim 1 , further comprising:

identifying the sets of features for each of the rendered images.

7. The computer-implemented method of claim 1 , wherein the reference parameter value comprises a value associated with the parameter indicated as a desired value.

8. The computer-implemented method of claim 1 further comprising:

generating a new image using the identified parameter image adjustment or the reference parameter value;

selecting a second parameter to be analyzed in association with the new image;

identifying a second set of candidate parameter values associated with the second parameter;

rendering the new image in accordance with each candidate parameter value in the second set of candidate parameter values to generate a second set of rendered images;

using a second neural network to identify a second parameter image adjustment to apply to the new image based on features associated with the second set of rendered images; and

training the second neural network based on a comparison of the second parameter image adjustment and a second reference parameter value associated with the second parameter being analyzed.

9. One or more computer-readable storage media having a plurality of executable instructions embodied thereon, which, when executed by one or more processors, cause the one or more processors to perform a method for identifying parameter image adjustments, the method comprising:

identifying a set of candidate parameter values associated with a parameter to be analyzed in association with an image;

rendering the image in accordance with each candidate parameter value to generate a set of rendered images;

generating an analysis vector for each of the rendered images based on corresponding sets of features identified for each of the rendered images;

concatenating the analysis vectors into a composite analysis vector; and

inputting the composite analysis vector into a neural network to identify a parameter image adjustment to apply to the image.

10. The media of claim 9 , the method further comprising:

selecting the parameter to be analyzed based on a predetermined sequence of parameters, a random selection of parameters, an algorithm, or parameter selection neural network.

11. The media of claim 9 , wherein the set of candidate parameter values comprises a set of equally spaced values within an interval, a set of symmetrically spaced values about a nominal value, or randomly spaced values within an interval.

12. The media of claim 9 , further comprising:

selecting the neural network based on the parameter being analyzed.

13. The media of claim 9 , further comprising:

identifying the sets of features for each of the rendered images.

14. The media of claim 9 , the method further comprising:

generating a new image using the identified parameter image adjustment;

selecting a second parameter to be analyzed in association with the new image;

identifying a second set of candidate parameter values associated with the second parameter;

rendering the new image in accordance with each candidate parameter value in the second set of candidate parameter values to generate a second set of rendered images;

analyzing the second set of rendered images to generate a second composite analysis vector representing features associated with the second set of rendered images; and

inputting the second composite analysis vector into a second neural network to identify a second parameter image adjustment to apply to the new image.

15. The media of claim 9 , wherein the identified parameter image adjustment is provided as a recommended adjustment for the parameter.

16. The media of claim 9 , wherein the identified parameter image adjustment is applied to the image.

17. The media of claim 14 , wherein the parameter image adjustment and the second parameter image adjustment are provided as recommended adjustments for the corresponding parameters.

18. The media of claim 14 , wherein the parameter image adjustment and the second parameter image adjustment are applied to the image.

19. A computing system comprising:

means for training a neural network system, wherein the neural network system includes a set of neural networks sequentially trained using image variation to identify parameter image adjustments for images; and

means for identifying parameter image adjustments for an input image using the trained neural network system, the parameter image adjustments identified based on concatenated analysis vectors generated from a set of rendered images based on corresponding sets of features identified for each of the rendered images.

20. The system of claim 19 , wherein each of the neural networks is trained based on a comparison of a parameter image adjustment identified by the corresponding neural network and a reference parameter value associated with the parameter for which the parameter image adjustment is identified.