Disney Enterprises, Inc.

Kari M Grubin

Leon Silverman

Nimesh C Narayan

Yazmaliza Yaacob

Andrew J Wahlquist

Anne Chang

Jeffrey Miller

Christopher Witham

Techniques are disclosed for converting image frames from one color space to another while predicting artistic choices that a director, colorist, or others would make. In one configuration, a color conversion application receives image frames, an indication of color spaces to convert between, and metadata associated with the image frames and/or regions therein. The conversion application determines a global, base color conversion for the image frames using a predefined color space transformation. Then, the conversion application (optionally) extracts image regions depicting objects of interest in the image frames, after which the color conversion application processes each of the extracted image regions and the remaining image frames (after the extracted regions have been removed) using one or more functions determined using machine learning. The processed extracted regions and remainders of the image frames are then combined by the color conversion application for output.

Machine learning color science conversion

Techniques are disclosed for converting image frames, such as the image frames of a motion picture, from one aspect ratio to another while predicting the pan and scan framing decisions that a human operator would make. In one configuration, one or more functions for predicting pan and scan framing decisions are determined, at least in part, via machine learning using training data that includes historical pan and scan conversions. The training data may be prepared by extracting features indicating visual and/or audio elements associated with particular shots, among other things. Function(s) may be determined, using machine learning, that take such extracted features as input and output predicted pan and scan framing decisions. Thereafter, the image frames of a received video may be converted between aspect ratios on a shot-by-shot basis, by extracting the same features and using the function(s) to make pan and scan framing predictions.

Aspect ratio conversion with machine learning

Systems for electronic media distribution includes a differential versioning server configured to receive a first media file including a first set of data with a first set of attributes and a second media file including a second set of data with a second set of attributes, generate a first differential data file as a function of differences between the first media file and the second media file, and generate a first differential metadata file including an encoding data set configured to enable a media decoder to regenerate the second media file by applying the first differential data file to the first media file. Systems for electronic media distribution may also include a receiver communicatively coupled to a differential versioning decoder configured to receive the first media file and the first differential data file and generate the second media file by applying the first differential data file to the first media file.

Systems and methods for differential media distribution

Growing community of inventors

Woodland Hills, CA, United States of America

Kari M Grubin