Realnetworks Gmbh

Reza Rassool

Embodiments are directed towards video enhancement in accordance with time constraint. An example method includes determining a time constraint for transforming low dynamic range (LDR) video content to high dynamic range (HDR) video content, processing the LDR video content to generate instructions for transforming to HDR video content in accordance with the time constraint, rendering the HDR video content based on executing the generated instructions; and producing metadata including the generated instructions for sharing.

Real-time video enhancement and metadata sharing

Systems and methods are provided for determining a candidate identity of a person in video data based on a biometric motion signature corresponding to motion of a face of the person in the video data. Motion information is obtained corresponding to motion of the face in the video data. The motion information is provided as input to a neural network and the neural network generates the biometric motion signature in response. The biometric motion signature is compared with a plurality of biometric signatures stored in data storage. The candidate identity of the person is determined as a result of a correspondence between the biometric motion signature and a stored biometric signature of the plurality of biometric signatures.

Methods and systems for facial recognition using motion vector trained model

Embodiments are directed towards hue-based video enhancement. An example method includes processing low dynamic range (LDR) video content to generate an inverse tone map (ITM) for transforming the LDR video content to high dynamic range (HDR) video content, converting the LDR video content into Hue, Saturation and Lightness (HSY) color space to produce H-channel data, S-channel data, and Y-channel data, de-noising the H-channel data, remapping the de-noised H-channel data, the S-channel data, and the Y-channel data based on the ITM; and rendering the HDR video content based thereon.

Hue-based video enhancement and rendering

Embodiments are directed towards metadata service for video enhancement. An example method includes receiving a request for metadata applicable to instruct a display device to transform low dynamic range (LDR) video content to high dynamic range (HDR) video content, determining availability of candidate metadata, and responsive to determining that no candidate metadata is available, providing options for generating the metadata applicable to instruct the display device. The example method can also include causing generation of the metadata based at least one of the options, and providing the generated metadata to the display device.

Intelligent metadata service for video enhancement

Systems and methods for enabling a video capture/encoding device to capture frames of a video sequence; obtain feedback from a video decoding/rendering device; divide each frame into an array of patches; select one or more patches of each frame for supplemental processing; cause the frames of the video sequence to be encoded by a video encoding module and cause the selected patches to be processed by one or more supplemental processing module(s); and assemble a multi-layer video transport stream wherein, for each frame of the video sequence, a base layer of the transport stream includes data corresponding to an encoded version of the current frame and one or more supplemental layers of the transport stream include meta-data corresponding to the division of the current frame into patches and data corresponding to the output of the supplemental processing of the selected patches of the current frame.

Growing community of inventors

Seattle, WA, United States of America

Reza Rassool