Abstract: A viewer fovea tracking generator is configured to analyze image data corresponding to a viewing of the video program via an A/V player by at least one viewer, and to generate fovea tracking data corresponding to the at least one viewer. A network interface configured to transmit the fovea tracking data to the video source via a network. The video program is fovea encoded by the video source in accordance with the fovea tracking data.

Abstract: Aspects of the subject disclosure may include, for example, a system that includes a signal interface configured to receive a plurality of video signals from a corresponding plurality of video cameras. A surveillance processor is configured to process the plurality of video signals and to recognize at least one person in at least one of the plurality of video signals and an emotional state corresponding to the at least one person and that generates surveillance data corresponding to the at least one person, based on the emotional state corresponding to the at least one person. Other embodiments are disclosed.

Abstract: A user interest analysis generator analyzes input data corresponding to a viewing of the video program via the A/V player by at least one viewer, to determine a period of interest corresponding to the at least one viewer and to generate viewer interest data that indicates the period of viewer interest. A metadata selection generator processes the viewer interest data and time coded metadata corresponding to the video program, and selects portions of the time coded metadata, corresponding to content of the video program during the period of interest, for display to the viewer by a display device.

Abstract: A user interest analysis generator analyzes input data corresponding to a viewing of the video program via the A/V player by at least one viewer, to determine a period of interest corresponding to the at least one viewer and to generate viewer interest data that indicates the period of viewer interest. An ad selection generator processes the viewer interest data and time coded metadata corresponding to the video program to automatically retrieve an advertisement from a remote ad server, corresponding to content of the video program during the period of interest, for display to the viewer by a display device.

Abstract: A user interest analysis generator analyzes input data corresponding to a viewing of the video program via the A/V player by at least one viewer, to determine a period of interest corresponding to the at least one viewer and to generate viewer interest data that indicates the period of viewer interest. A social media generator processes the viewer interest data and time coded metadata corresponding to the video program to automatically generate a social media post, corresponding to content of the video program during the period of interest, for posting to a social media account associated with the at least one viewer.

Abstract: A transform size determination module includes a transform size preprocessor configured to process residual data from a picture of the video data to evaluate a plurality of transform block sizes and to generate candidate transform size data that indicates a selected non-null proper subset of the plurality of transform block size. A final transform size determination processor generates final transform block size data that indicates a final transform block size, based on the candidate transform size data.

Abstract: A video decoder includes an entropy decoding device that generates entropy decoded (EDC) data from an encoded video signal. A multi-format video decoding device includes a plurality of vector processor units that generate a decoded video signal from the EDC data. The plurality of vector processing units includes at least one filter vector processor that operates in conjunction with a plurality of programmable filter parameters.

Abstract: An intra-prediction module includes an intra-prediction preprocessor configured to process pixel data from blocks of picture data to determine edge strength data and edge angle range data corresponding to a plurality of pixels of the blocks, and further to generate intra-prediction candidate data based on the edge strength data and the edge angle range data. The intra-prediction candidate data indicates a first subset selected from a plurality of intra-prediction partitions and a second subset selected from a plurality of intra-prediction modes. A rate distortion optimization processor determines final intra-prediction data, based on the intra-prediction candidate data.

Abstract: A weighted prediction module includes a weighted prediction parameter generation module configured to generate a plurality of initial weighted prediction parameters, to analyze the plurality of initial weighted prediction parameters, and to generate a refinement flag that indicates one of: enable parameter refinement and disable parameter refinement. A weighted prediction parameter refinement module is configured to generate a plurality of refined weighted prediction parameters by refining the plurality of initial weighted prediction parameters, when the refinement flag indicates that the parameter refinement is enabled. A weighted prediction flag generation module is configured to generate a weighted prediction flag that indicates one of: enable weighted prediction and disable weighted prediction, based on the refinement flag.

Abstract: An intra-prediction module includes a candidate screening preprocessor configured to process blocks of picture data based on intra-prediction candidate data corresponding to a plurality of pixels of the blocks. The intra-prediction candidate data indicates a first subset selected from a plurality of intra-prediction partitions and a second subset selected from a plurality of intra-prediction modes. The processing includes screening the intra-prediction candidate data to generate screened intra-prediction candidate data that indicates a third subset selected from a plurality of intra-prediction partitions and a fourth subset selected from a plurality of intra-prediction modes. A rate distortion optimization processor determines final intra-prediction data, based on the screened intra-prediction candidate data.

Abstract: A video encoder includes a motion search module that determines a motion search motion vector for a region of a selected picture of the plurality of pictures. The motion search module determines the region by merging selected ones of a plurality of blocks of the selected picture based on an evaluation of a cost matrix associated with the plurality of blocks of the selected picture.

Abstract: A metric representing the sum of variances for pixel blocks of a region of an image are used to identify the presence a video feature of the image, and a transcoding is performed responsive to identifying the presence of the video feature. The identified video feature can include, but is not limited to, a scene change, the presence of a black border region or a caption region, or the complexity of the image. The transcoding operation can include, but is not limited to, coding the image as an Intra-frame, omitting the content corresponding to the black border region or the caption region from the transcoded image or allocating a relatively lower bit budget for the black border region or a relatively higher bit budget to the caption region during transcoding of the image, or setting the bit budget for rate control during transcoding.

Abstract: Aspects of the subject disclosure may include, for example, a system that includes a pattern recognition module for generating index data describing content of an image sequence that is time-coded to the image sequence. The pattern recognition module generates the index data based on coding feedback data that includes color histogram data and further based on audio data. A video codec generates a processed video signal based on the image sequence and by generating the color histogram data in conjunction with the processing of the image sequence. Other embodiments are disclosed.

Abstract: An entropy coding module is provided for use in a video encoder that encodes a video input signal based on a plurality of macroblocks derived from the video input signal. The entropy coding module includes an entropy coder that generates entropy encoded data from discrete transformed coefficients for the plurality of macroblocks. A neighbor management module stores neighbor data for at least one macroblock of the plurality of macroblocks for retrieval by the entropy encoder, when operating on at least one neighboring macroblock of the plurality of macroblocks.

Abstract: A video processing device includes a complexity estimation module to determine a first sum of variances metric and a first estimated picture encoding cost metric for a first picture of a video stream. The video processing device further includes a scene analysis module to determine a first threshold based on a first statistical feature for sum of variance metrics of a set of one or more pictures preceding the first picture in the video stream and a second threshold based on a second statistical feature for estimated picture encoding cost metrics of the set of one or more pictures. The scene analysis module further is to identify a scene change as occurring at the first picture based on the first sum of variances metric, the first estimated picture encoding cost metric, the first threshold, and the second threshold.

Abstract: A video processing device includes a rate control module to determine more accurate initial quantization parameters at each scene switching point and to adjust the QP parameters in response to scene changes using a sum of variances metric and an estimated picture encoding cost metric from a coding complex estimation block. To determine a first quantization parameter set, a sum of variances metric and an estimated picture encoding cost metric for an initial set pictures of a video stream are used. A bit allocation module is to set a target bit allocation for infra-encoded pictures as substantially proportional to the sum of variances metric and substantially inversely proportional to the estimated picture encoding cost metric, and set a target bit allocation for forward predictive and bi-predictive pictures as substantially proportional to the estimated picture encoding cost metric and substantially inversely proportional to the sum of variances metric.

Abstract: A video decoder includes an entropy decoding device that generates entropy decoded (EDC) data from an encoded video signal. A multi-format video decoding device includes a plurality of vector processor units that generate a decoded video signal from the EDC data. The plurality of vector processing units are programmed via VPU instructions formatted to include a vector instruction portion, a scalar instruction portion, and a branching instruction portion.

Abstract: An entropy coding module is provided for use in a video encoder that encodes a video input signal based on a plurality of macroblocks derived from the video input signal. The entropy coding module includes an entropy coder that generates entropy encoded data from discrete transformed coefficients for the plurality of macroblocks. A neighbor management module stores neighbor data for at least one macroblock of the plurality of macroblocks for retrieval by the entropy encoder, when operating on at least one neighboring macroblock of the plurality of macroblocks.

Abstract: A technique for encoding and decoding video information uses a plurality of video processing modules (VPMs), whereby each video processing module is dedicated to a particular video processing function, such as filtering, matrix arithmetic operations, and the like. Information is transferred between the video processing modules using a set of first-in first-out (FIFO) buffers. For example, to transfer pixel information from a first VPM to a second VPM, the first VPM stores the pixel information at the head of a FIFO buffer, while the second VPM retrieves information from the tail of the FIFO buffer. The FIFO buffer thus permits transfer of information between the VPMs without storage of the information to a cache or other techniques that can reduce video processing speed.

Abstract: A video processing device includes a video processing unit that decodes a video input signal into a decoded video signal in accordance with a video compression protocol, based on uncompressed video frame data. A tile engine includes a tile accumulation module that accumulates the uncompressed video frame data into a plurality of tile units, wherein each of the plurality of tile units includes a plurality of video span units. A tile compression/decompression module generates compressed video frame data for storage in a compressed video frame buffer by compressing the plurality of video span units into a plurality of compressed video span units and further that retrieves the compressed video frame data from the compressed video frame buffer by retrieving the plurality of compressed video span units and generating the uncompressed video frame data by decompressing the plurality of compressed video span units.