US 12,248,949 B2
Media content enhancement based on user feedback of multiple variations
Trisha Mittal, San Jose, CA (US); Viswanathan Swaminathan, Saratoga, CA (US); Ritwik Sinha, Cupertino, CA (US); Saayan Mitra, San Jose, CA (US); David Arbour, San Jose, CA (US); and Somdeb Sarkhel, San Jose, CA (US)
Assigned to Adobe Inc., San Jose, CA (US)
Filed by ADOBE INC., San Jose, CA (US)
Filed on Nov. 4, 2021, as Appl. No. 17/519,311.
Prior Publication US 2023/0139824 A1, May 4, 2023
Int. Cl. G06Q 30/0201 (2023.01); G06N 20/00 (2019.01)
CPC G06Q 30/0201 (2013.01) [G06N 20/00 (2019.01)] 17 Claims
OG exemplary drawing
 
1. A non-transitory computer readable medium storing computer-usable instructions that, when used by one or more processors, cause the one or more processors to perform operations comprising:
receiving a media content item that is an image of a plurality of pixel values; converting the media content item into a first feature vector representative of the plurality of pixel values in search space:
receiving an indication that a user has set a boundary or range of parameter values for which a model will generate variations from:
based on the boundary or range and the first feature vector, automatically generating a plurality of variations of the media content item by automatically changing in the search space, the first feature vector representative of a change in at least one of, vibrance, saturation, brightness, contrast, or sharpness of one or more of the plurality of pixel values, each variation, of the plurality of variations, being a different version of the image; receiving explicit user feedback for each variation of the plurality of variations, wherein the explicit user feedback corresponds to a scaled user rating of a respective variation of the plurality of variations according to an aesthetic preference for the respective variation; based on the explicit user feedback, scoring each variation of the plurality of variations according to the scaled user rating of the respective variation; based on the changing, in the search space, the first feature vector and the scoring of each variation, automatically generating, via a Bayesian Optimization Model a first variation of the image based on using a surrogate function that models an objective function representing a true distribution of user feedback for the plurality of variations by sampling, in the search space, at least a second feature vector representing the first variation based on minimizing a distance to the objective function evaluated at a maximum, wherein the first variation represents the maximum of the objective function and the maximum indicates a highest scoring variation according to the user feedback; based on the generating of the first variation, generating an output image of pixel values that represent the first variation; and based on the generating of the output image, causing
presentation, at a computing device associated with the user, of the output image.