Abstract: Disclosed herein are system, method and/or computer program product embodiments, and/or combinations and sub-combinations thereof, for performing context classification of streaming content using machine learning (ML). In an embodiment, a streaming media client receives an audio/video (A/V) stream that represents a portion of content to be played back by the client. The client reconstructs a sequence of video frames from the A/V stream, extracts audio information from the A/V stream, and executes an ML based classifier to predict a context label associated with the portion of content based at least on one or more video frames from the sequence of video frames and the audio information. The client then transmits the context label to a streaming media service. The service may use the context label to select an advertisement or content recommendation to send to the client or to select a set of content streaming parameters for the client.

Abstract: Disclosed herein are system, method and/or computer program product embodiments, and/or combinations and sub-combinations thereof, for performing context classification of streaming content using machine learning (ML). In an embodiment, a streaming media client receives an audio/video (A/V) stream that represents a portion of content to be played back by the client. The client reconstructs a sequence of video frames from the A/V stream, extracts audio information from the A/V stream, and executes an ML based classifier to predict a context label associated with the portion of content based at least on one or more video frames from the sequence of video frames and the audio information. The client then transmits the context label to a streaming media service. The service may use the context label to select an advertisement or content recommendation to send to the client or to select a set of content streaming parameters for the client.

Abstract: The invention provides a method for evaluating a set of input data, the input data comprising at least one of: clinical data of a subject; genomic data of a subject; clinical data of a plurality of subjects; and genomic data of a plurality of subjects, using a deep learning algorithm. The method includes obtaining a set of input data, wherein the set of input data comprises raw data arranged into a plurality of data clusters and tuning the deep learning algorithm based on the plurality of data clusters. The deep learning algorithm comprises: an input layer; an output layer; and a plurality of hidden layers. The method further includes performing statistical clustering on the raw data using the deep learning algorithm, thereby generating statistical clusters and obtaining a marker from each statistical cluster. Finally, the set of input data is evaluated based on the markers to derive data of medical relevance in respect of the subject or subjects.

Abstract: Disclosed herein are system, method and/or computer program product embodiments, and/or combinations and sub-combinations thereof, for enhancing a picture quality of visual content rendered for display by a media device. In an embodiment, the media device reconstructs a video frame from a video signal that is received by the media device, provides the video frame as input to a machine learning model that outputs a set of picture quality parameter values based on the video frame, receives the set of picture quality parameter values output by the machine learning model, modifies the video frame based on the set of picture quality parameter values to generate a modified video frame, and provides the modified video frame to a display device for presentation thereby.

Abstract: Disclosed herein are system, method and/or computer program product embodiments, and/or combinations and sub-combinations thereof, for performing context classification of streaming content using machine learning (ML). In an embodiment, a streaming media client receives an audio/video (A/V) stream that represents a portion of content to be played back by the client. The client reconstructs a sequence of video frames from the A/V stream, extracts audio information from the A/V stream, and executes an ML based classifier to predict a context label associated with the portion of content based at least on one or more video frames from the sequence of video frames and the audio information. The client then transmits the context label to a streaming media service. The service may use the context label to select an advertisement or content recommendation to send to the client or to select a set of content streaming parameters for the client.

Abstract: The invention provides a method for evaluating a set of input data, the input data comprising at least one of: clinical data of a subject; genomic data of a subject; clinical data of a plurality of subjects; and genomic data of a plurality of subjects, using a deep learning algorithm. The method includes obtaining a set of input data, wherein the set of input data comprises raw data arranged into a plurality of data clusters and tuning the deep learning algorithm based on the plurality of data clusters. The deep learning algorithm comprises: an input layer; an output layer; and a plurality of hidden layers. The method further includes performing statistical clustering on the raw data using the deep learning algorithm, thereby generating statistical clusters and obtaining a marker from each statistical cluster. Finally, the set of input data is evaluated based on the markers to derive data of medical relevance in respect of the subject or subjects.

Abstract: The invention provides a method for evaluating a set of input data, the input data comprising at least one of: clinical data of a subject; genomic data of a subject; clinical data of a plurality of subjects; and genomic data of a plurality of subjects, using a deep learning algorithm. The method includes obtaining a set of input data, wherein the set of input data comprises raw data arranged into a plurality of data clusters and tuning the deep learning algorithm based on the plurality of data clusters. The deep learning algorithm comprises: an input layer; an output layer; and a plurality of hidden layers. The method further includes performing statistical clustering on the raw data using the deep learning algorithm, thereby generating statistical clusters and obtaining a marker from each statistical cluster. Finally, the set of input data is evaluated based on the markers to derive data of medical relevance in respect of the subject or subjects.

Abstract: The invention provides a method for evaluating a set of input data, the input data comprising at least one of: clinical data of a subject; genomic data of a subject; clinical data of a plurality of subjects; and genomic data of a plurality of subjects, using a deep learning algorithm. The method includes obtaining a set of input data, wherein the set of input data comprises raw data arranged into a plurality of data clusters and tuning the deep learning algorithm based on the plurality of data clusters. The deep learning algorithm comprises: an input layer; an output layer; and a plurality of hidden layers. The method further includes performing stabstical clustering on the raw data using the deep learning algorithm, thereby generating statistical clusters and obtaining a marker from each statistical cluster. Finally, the set of input data is evaluated based on the markers to derive data of medical relevance in respect of the subject or subjects.

Abstract: A system and method to render 3D images from a 2D source are described. An embodiment of a method to render 3D images from a 2D source comprises the steps of providing a graphics rendering device to estimate depth of a 2D image; providing video or graphics textures and depth-maps to describe an object in a 3D scene; creating, in one embodiment, a single view angle and in another preferred embodiment at least two view angles of the 3D scene to represent an intraocular distance using the graphics rendering device; and presenting both of the at least two view angles on a display using the graphics rendering device and especially the commonly available 3D imaging technology of the graphics rendering device.

Abstract: An apparatus and method for enhancing the quality of a digital video signal uses coding information for use in connection with spatial domain sharpness enhancement algorithms used in multimedia devices. The sharpness of previously encoded or transcoded digital video images is enhanced without enhancing encoding artifacts. The apparatus includes a usefulness metric generator that identifies a limit to the sharpness enhancement of an image that can be applied to a previously coded digital video that represents a sequence of images without enhancing coding artifacts. The usefulness metric generator applies the usefulness metric to at least one sharpness enhancement algorithm. The usefulness metric and the sharpness enhancement algorithm are separate so that the usefulness metric can be used with a variety of video enhancement algorithms.

Abstract: In accordance with the preferred embodiment of the present invention, a method of and system for improving temporal consistency of an enhanced signal representative of at least one frame using a sharpness enhancement algorithm with an enhancement gain are provided. The method comprises the steps of: receiving the enhanced signal comprising at least one frame, obtaining an enhancement gain for each pixel in the frame, retrieving an enhancement gain value of each pixel in a reference frame from a gain memory using motion vectors, identifying if the frame is an I, P or B frame type and determining an updated enhancement gain for an I frame type by calculating a gain map for use in the sharpness enhancement algorithm. The updated enhancement gain of each pixel is equal to enhancement gain previously determined for use in the sharpness enhancement algorithm.

Abstract: Pixels in a video image may be segmented based upon selected criteria, such as a common color, texture, shape, amplitude range or temporal variation. Color values for these pixels may be used to calculate a color probability function which indicates the probability that the color value of the pixel will lie within a designated range of values. The pixels are also used to calculate a texture probability function that indicates whether the pixel represents a designated texture. Pixels that are assigned to a given segment may then be further processed to improve the quality of an image. In this manner, pixels that identify grass, sky, human skin, etc. may be identified and processed in order to achieve a more pleasing appearance.

Abstract: In accordance with the preferred embodiment of the present invention, a method of and system for improving temporal consistency of an enhanced signal representative of at least one frame using a sharpness enhancement algorithm with an enhancement gain are provided. The method comprises the steps of: receiving the enhanced signal comprising at least one frame, obtaining an enhancement gain for each pixel in the frame, retrieving an enhancement gain value of each pixel in a reference frame from a gain memory using motion vectors, identifying if the frame is an I, P or B frame type and determining an updated enhancement gain for an I frame type by calculating a gain map for use in the sharpness enhancement algorithm. The updated enhancement gain of each pixel is equal to enhancement gain previously determined for use in the sharpness enhancement algorithm.

Abstract: Pixels in a video image may be segmented based upon selected criteria, such as a common color, texture, shape, amplitude range or temporal variation. Color values for these pixels may be used to calculate a color probability function which indicates the probability that the color value of the pixel will lie within a designated range of values. The pixels are also used to calculate a texture probability function that indicates whether the pixel represents a designated texture. Pixels that are assigned to a given segment may then be further processed to improve the quality of an image. In this manner, pixels that identify grass, sky, human skin, etc. may be identified and processed in order to achieve a more pleasing appearance.

Abstract: There is disclosed an apparatus and method for boundary detection in vector sequences and edge detection in color image signals. A boundary detection controller analyzes a vector sequence that represents a signal. A frequency dependent function is used to calculate a modified first order difference (MFD) of the vector act sequence, first as a vector quantity, then as a scalar quantity. A local maximum of the MFD scalar quantity that is greater than a predetermined threshold value identifies a boundary location. The boundary detection controller also analyzes luminance and chrominance portions of a color image signal to locate luminance edges and chrominance edges in a color image.

Abstract: An apparatus and method is disclosed for enhancing the quality of a digital video signal using coding information. The sharpness of previously encoded or transcoded digital video images is enhanced without enhancing encoding artifacts. The invention may be used in connection with spatial domain sharpness enhancement algorithms used in multimedia devices. The apparatus comprises a usefulness metric generator that identifies a limit to the sharpness enhancement of an image that can be applied to a previously coded digital video that represents a sequence of images without enhancing coding artifacts. The usefulness metric generator applies the usefulness metric to at least one sharpness enhancement algorithm. The usefulness metric and the sharpness enhancement algorithm are separate so that the usefulness metric can be used with a variety of video enhancement algorithms.