Disney Enterprises, Inc.

Nimesh C Narayan

Anthony M Accardo

Miquel Angel Farre Guiu

Yazmaliza Yaacob

Lena Volodarsky Bareket

Leonid Sigal

Marc Junyent Martin

Aljoscha Smolic

Matthew C Petrillo

Alexis J Lindquist

Jordi Pont-Tuset

Kari M Grubin

Andrew J Wahlquist

Jack Luu

Alan Pao

Pablo Beltran

Katharine S Ettinger

Miguel Angel Farre Guiu

Katharine (Kaki) S Ettinger

David Lindsay

Yoomi Koh Poon

There is provided a system including a non-transitory memory storing an executable code and a hardware processor executing the executable code to receive a media content including a plurality of frames, divide the media content into a plurality of shots, each of the plurality of shots including a plurality of frames of the media content based on a first similarity between the plurality of frames, determine a plurality of sequential shots of the plurality of shots to be part of a first sub-scene of a plurality of sub-scenes of a scene based on a timeline continuity of the plurality of sequential shots, identify each of the plurality of shots of the media content and each of the plurality of sub-scenes with a corresponding beginning time code and a corresponding ending time code.

Systems and methods for intelligent media content segmentation and analysis

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

There is provided a system including a computing platform having a hardware processor and a memory, and a metadata extraction and management unit stored in the memory. The hardware processor is configured to execute the metadata extraction and management unit to extract a plurality of metadata types from a media asset sequentially and in accordance with a prioritized order of extraction based on metadata type, aggregate the plurality of metadata types to produce an aggregated metadata describing the media asset, use the aggregated metadata to include at least one database entry in a graphical database, wherein the at least one database entry describes the media asset, display a user interface for a user to view tags of metadata associated with the media asset, and correcting presence of one of the tags of metadata associated with the media asset, in response to an input from the user via the user interface.

Metadata extraction and management

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

According to various embodiments of the disclosed technology, it is possible for content owners to better control the presentation of owned multimedia assets. Implementations of embodiments of the technology of the present disclosure leverage the specificity and greater breadth of detailed information about the multimedia asset of temporal metadata to enable content owners to associate business rules with particular content elements of the multimedia asset. After being notified of a match through the traditional fingerprinting process, embodiments in accordance with the present disclosure can programmatically identify the exact content elements contained within the uploaded multimedia file, and accordingly apply the applicable rules. Content owners may create a list of business rules associated with particular temporal metadata tags of a multimedia assets so that fine-grained rule application is possible.

Systems and methods for automated application of business rules using temporal metadata and content fingerprinting

There are provided systems and methods for performing metadata extraction and management. Such a system includes a computing platform having a hardware processor, a system memory, and metadata extraction and management unit stored in the system memory. The system is configured to extract multiple metadata types from a media asset, and to aggregate the multiple metadata types to produce an aggregated metadata describing the media asset. The system is further configured to transform the aggregated metadata into at least one database entry identifying the media asset, and to map the at least one database entry into a graphical database so as to relate the media asset to at least one other media asset represented in the graphical database.

Methods and systems of archiving media files are provided. Media files may be archived such that only the difference between a media file and a base media file is stored. The base media file is archived. The media file to be archived and the base media file may have common attributes such as video codec, resolution, frame rate, and/or color space. A media file to be archived may be compared to the base media file to determine any difference. Bit-to-bit analysis or frame-to-frame analysis may be performed to identify the differences between a media file to be archived and a base media file. The differences may be extracted from the media file to be archived. A difference media file may be created to store the difference of a media file with respect to the base media file. A record may be created to store the actual location where the difference is extracted from a media file to be archived.

Methods and systems of archiving media files

Growing community of inventors

Los Angeles, CA, United States of America

Nimesh C Narayan