Google Inc.

Krzysztof Kulewski

Sevket Derin Babacan

Aravind Krishnaswamy

Sergey Ioffe

Stephen J DiVerdi

John Thomas Nack

Juan Carlos Miguel Anorga

Jordi Miguel Verdu Orts

Daniel Fang

A method generates a compressed video that is consistent across different devices. The method comprises identifying an output bitrate. The method further comprises parsing parameters of an input video. The method further comprises generating a blank video with a fixed duration based on the parameters of the input video. The method further comprises generating a representative video based on providing the blank video as input to a decoder. The method further comprises determining a request bitrate for the representative video and the output bitrate. The method further comprises compressing the input video using the request bitrate to generate an actual video.

Safe and cross device consistent video compression

Implementations generally relate to providing regions of interest in an image. In some implementations, a method includes receiving an image. The method further includes determining one or more image features. The method further includes grouping the one or more image features into one or more regions of interest.

Providing regions of interest in an image

Implementations relate to computer-assisted cropping of an image. A computer-executed method includes receiving user input indicative of a change in size of a crop window from a first size to a second size, where the crop window is displayed over an image in an image editing user interface. The method detects that the crop window of the second size has an aspect ratio within a threshold range of a predetermined aspect ratio. In response to the detection, the method resizes the crop window to a third size, where the crop window of the third size has a resized aspect ratio substantially the same as the predetermined aspect ratio.

Computer-assisted image cropping

Implementations relate to visualizing and measuring impact of image modifications. In some implementations, a method to measure and indicate impact of image modification includes applying an edit operation to a first image, including modifying one or more pixels of the first image to provide a modified image. The method determines an impact score associated with the edit operation and indicative of a degree of visual impact of the edit operation to the first image. The method provides, based on the impact score, the modified image in a visualization of image modification for the first image, and provides the visualization for display by a display device.

Measuring and visualizing impact of image modifications

Implementations relate to adjusting images using a texture mask. In some implementations, a method includes detecting one or more texture regions having detected texture in an image, and generating a mask from the image based on the detected texture regions. The detected texture regions are distinguished in the mask from other regions of the image that do not have detected texture. The method applies one or more adjustment operations to the image in amounts based on values of the mask.

Image adjustment using texture mask

Aspects of the subject technology relate to automatically and selectively applying a fill light filter to an image. A process includes determining an edge-preserved, smoothed version of the image, and determining a grayscale version of the image. The process also includes comparing each pixel of the edge-preserved, smoothed version of the image to each corresponding pixel of the grayscale version of the image. The process also includes applying the fill light filter to the image based on the comparison. The fill light filter is automatically adjusted based on identifying regions in the image. Selectively applying the fill light filter can reduce artifacts and noise from forming or being amplified as a result of the fill light filter.

Automated selective application of fill light filter

Systems, methods and computer readable media for determination of monochromatic images are described. Some implementations can include a method. The method can include converting an image in a first colorspace to a first converted image in a second colorspace. The method can also include generating a hue histogram based on hue values in the converted image and determining a hue dispersion measure based on the hue histogram and a number of pixels in the image. The method can further include determining a hue clustering value based on the hue histogram and converting the image in the first colorspace into a second converted image in a third colorspace. The method can also include determining a variance measure based on the second converted image, and comparing the hue dispersion measure, the hue clustering value and the variance measure to a first threshold value, a second threshold value and a third threshold value, respectively.

Monochromatic image determination

Proprietary rights logos are detected in a video. The video is divided into a plurality of regions that are analyzed for generic proprietary rights logo features. A confidence mask is generated that comprises a plurality of scaling factors, each scaling factor corresponding to a region of the video and indicating a likelihood that the corresponding region of the video includes a proprietary rights logo. The scaling factors of the confidence mask are applied to the video data to generate an altered video. The altered video is analyzed to determine a confidence measure that the video includes a reference proprietary rights logo.

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Sunnyvale, CA, United States of America

Krzysztof Kulewski