Google Inc.

Fujifilm Corporation

Troy Chinen

Sergey Ioffe

Vivek Kwatra

George Dan Toderici

Hartwig Adam

Corinna Cortes

Ameesh Makadia

Teresa Ko

Saurabh Singh

David Charles Minnen

Thomas Leung

Johannes Balle

Sung Jin Hwang

Nemanja Petrovic

Sebastian Pueblas

Matthew Stephen Steiner

Matthias Grundmann

Erik Murphy-Chutorian

Shengyang Dai

John Patrick Flynn

Nicholas M Johnston

Nicholas Butko

Hui Fang

Yichang Shih

Florian Kriener

Falk Sticken

Tom Binder

Steven Vandebogart

Nikolaos Trogkanis

Ingo Wehmeyer

John Spiegel

Yang Song

Samy Bengio

Eirikur Thor Agustsson

Alex Sukhanov

Olivier Bousquet

Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for compressing and decompressing data. In one aspect, a method comprises: processing data using an encoder neural network to generate a latent representation of the data; processing the latent representation of the data using a hyper-encoder neural network to generate a latent representation of an entropy model; generating an entropy encoded representation of the latent representation of the entropy model; generating an entropy encoded representation of the latent representation of the data using the latent representation of the entropy model; and determining a compressed representation of the data from the entropy encoded representations of: (i) the latent representation of the data and (ii) the latent representation of the entropy model used to entropy encode the latent representation of the data.

Data compression using conditional entropy models

An example computer-implemented method for determining a perceptual quality of a subject video content item is provided. The example method can include inputting a subject frame set from the subject video content item into a first machine-learned model. The example method can also include generating, using the first machine-learned model, a feature based at least in part on the subject frame set. The example method can also include outputting, using a second machine-learned model, a score indicating the perceptual quality of the subject video content item based at least in part on the feature.

Method for constructing a perceptual metric for judging video quality

Techniques for providing image search templates are provided. An image search template may be associated with an image search query to aid the user in capturing an image that will be appropriate for processing the search query. The template may be displayed as an overlay during an image capturing process to indicate an appropriate image capturing pose, range, angle, or other view characteristics that may provide more accurate search results. The template may also be used in the image search query to segment the image and identify features relevant to the search query. Images in an image database may be clustered using characteristics of the images or metadata associated with the images in order to establish groups of images from which templates may be derived. The generated templates may be provided to users to assist in capturing images to be used as search engine queries.

Providing image search templates

A method, computer program product, and computer system for identifying a first portion of a facial image in a first image, wherein the first portion includes noise. A corresponding portion of the facial image is identified in a second image, wherein the corresponding portion includes less noise than the first portion. One or more filter parameters of the first portion are determined based upon, at least in part, the first portion and the corresponding portion. At least a portion of the noise from the first portion is smoothed based upon, at least in part, the one or more filter parameters. At least a portion of face specific details from the corresponding portion is added to the first portion.

Image denoising system and method

Implementations generally relate to generating compositional media content. In some implementations, a method includes receiving a plurality of photos from a user, and determining one or more composition types from the photos. The method also includes generating compositions from the selected photos based on the one or more determined composition types. The method also includes providing the one or more generated compositions to the user.

Generating compositions

A system and method is provided that determines whether objects in one image are visually similar to objects in another image by replacing the images' backgrounds with other images, such as a solid color or an image with texture, and comparing the resulting histograms.

Image similarity determination

Methods, systems, and articles of manufacture for detecting placeholder images are disclosed. These include, accessing a collection of digital images, clustering the digital images to generate at least one of a plurality of exact-duplicate image clusters and a plurality of near-duplicate image clusters, and selecting one or more placeholder image clusters from at least one of the plurality of exact-duplicate image clusters or the plurality of near-duplicate image clusters.

Place holder image detection via image clustering

An exemplary illumination normalization method is provided which includes receiving an input image having at least one spurious edge directly resulting from illumination, performing anisotropic diffusion on the input image to form a diffusion image, and removing at the least one spurious edge using the diffusion image. Another embodiment consistent with the invention is an apparatus for performing illumination normalization in an image which includes a processor operably coupled to a memory storing input image data which contains an object of interest having at least one spurious edge directly resulting from illumination, a model of a representative object of interest, and functional processing units for controlling image processing, wherein the functional processing units further include a model based anisotropic diffusion module which predicts edge information regarding the object of interest based upon the model, and produces a reflectance estimation utilizing the predicted edge information.

Method and apparatus for diffusion based illumination normalization

A sequence of images depicts a foreground object in motion. A base image is selected, and the other images in the sequence are co-registered with the base image in order to align the images to a common coordinate system. A background image and a binary foreground mask are generated from the sequence of aligned images. By applying the foreground mask to a chosen one of the aligned images, a representation of the moving object is extracted. After blurring the background image, the extracted representation may be superimposed onto the blurred background image to produce a new image.

Segmenting images and simulating motion blur using an image sequence

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Newark, CA, United States of America

Troy Chinen