Facebook, Inc.

Facebook Technologies, LLC

Ryan David Mack

Apostolos Lerios

Mark E Zuckerberg

Raylene Kay Yung

Serkan Piantino

Paul M McDonald

Jeff Huang

Ryan Case

Dirk John Stoop

Steven Jay Young

Samuel Wharton Lessin

Lubomir Dimitrov Bourdev

Balmanohar Paluri

Arun Vijayvergiya

Josh Wiseman

Drew Hamlin

Nicholas Felton

Joshua Wiseman

Daniel Klatzko Gibson

Stanislav Funiak

Zizhuang Yang

Peter Avelino Ruibal

In one embodiment, a method comprises accessing a plurality of images stored in a data store, and for a first image of the plurality of images, determining a color distribution of the first image, wherein the color distribution of the first image is based on a frequency of one or more colors depicted in the first image. The method further comprises, based on the color distribution of the first image, assigning the first image to a particular image class, wherein the particular image class further comprises a second image, wherein the assigning of the second image to the particular image class is further based on a color distribution of the second image. The method further comprises based on at least the first image and the particular image class, determining a particular color profile, assigning the particular color profile to the first image and the second image.

Systems and methods for lossy compression of image color profiles

The invention provides a display interface in a social networking system that enables the presentation of information related to a user in a timeline or map view. The system accesses information about a user of a social networking system, including both data about the user and social network activities related to the user. The system then selects one or more of these pieces of data and/or activities from a certain time period and gathers them into timeline units based on their relatedness and their relevance to users. These timeline units are ranked by relevance to the user, and are used to generate a timeline or map view for the user containing visual representations of the timeline units organized by location or time. The timeline or map view is then provided to other users of the social networking system that wish to view information about the user.

Selecting social networking system user information for display via a timeline interface

The techniques introduced here include a system and method for transcoding multimedia content based on the results of content analysis. The determination of specific transcoding parameters, used for transcoding multimedia content, can be performed by utilizing the results of content analysis of the multimedia content. One of the results of the content analysis is the determination of image type of any images included in the multimedia content. The content analysis uses one or more of several techniques, including analyzing content metadata, examining colors of contiguous pixels in the content, using histogram analysis, using compression distortion analysis, analyzing image edges, or examining user provided inputs. Transcoding the multimedia content can include adapting the content to the constraints in delivery and display, processing and storage of user computing devices.

Methods and systems for differentiating synthetic and non-synthetic images

Capturing structured data about previous events from users of a social networking system

In one embodiment, an image containing a number of colors is received. The image is divided into a number of sections, and an importance value is determined for each section. For each section of the image, a frequency of each color is determined. For each color in the image, a weighted score is calculated based on the frequency of that color in a particular section, and the importance value in the particular section. The weighted scores are used to adjust an error metric used to determine an approximation function for a color profile to transform the image from a first color space to a second color space, where the error metric measures the difference between the approximation function and an ideal function curve for the transformation.

Techniques to allow for optimizing an image based on an operational context. An operational context including at least one attribute of a client associated with a user of a social networking system is determined. An optimized image is created based on the operational context for provision to the user. The determining the operational context may comprise receiving a client identifier and determining the at least one attribute of the client based on the client identifier. The determining the operational context may comprise receiving location coordinates and determining quality of a data connection based on the location coordinates. The determining the operational context may comprise determining at least one usage trend in at least one geographic locality. The optimized image is provided to an image repository associated with the at least one geographic locality. The operational context may comprise at least one user-defined setting.

Systems and methods for device-dependent image transformations

Displaying social networking system user information via a historical newsfeed

In one embodiment, an image is received by a computing device, the image corresponding to a first color space. A color profile is created for transforming the image from the first color space to a second color space, the creation of the color profile being based on an approximation function for transforming the image. The approximation function is determined by a polyline which comprises a number of line segments. The polyline is separated from an ideal function curve for transforming the image by an error value that is within a pre-determined threshold.

Techniques for accurate color representation of images stored within a social networking system. In an embodiment, an error metric and a target error threshold are determined. A binary search algorithm and a simulated annealing algorithm are performed. A color profile for transforming an image in a first color space to a second color space is created based on the binary search algorithm, the simulated annealing algorithm, the error metric, and the target error threshold. Determination of the error metric may comprise determining a frequency with which a color occurs in the image, assigning a weight to the color based on the frequency, and calculating the error metric based on the weight. Determination of the error metric may comprise dividing the image into a plurality of sections, assigning a plurality of importance values to the plurality of sections, and calculating the error metric based on the plurality of importance values.

Creation of a color profile of an image

Growing community of inventors

Waltham, MA, United States of America

Ryan David Mack