Abstract: Systems and methods herein are for a video encoder to be associated with a rate optimization distortion (RDO) module and a calibration module, where the RDO module may be to perform RDO for received frames of a media stream and may be to generate at least an RDO output that is based in part on quality measures between the received frames and decoded frames, and where the calibration module may be to provide an evaluation metric that is to scale or transform at least a range of the quality measures, with the scaling or transforming to potentially reduce an effect on a compression performed in the video encoder.

Abstract: Systems and methods herein are for at least one execution unit that can perform an inference using a machine learning (ML) model and that is coupled to a video encoder, where the ML model can determine a genre associated with received frames of a media stream based in part on using ML model features associated with different genres, where the video encoder can encode the media stream based in part on the determined genre.

Abstract: In one embodiment, a system includes a hardware accelerator to receive video data of multiple video frames, divide each of the video frames into respective blocks, compute encoding assist data including at least one video encoding parameter type for each of the respective blocks of each of the video frames, and store respective portions of the encoding assist data across respective database tables, and an interface to provide the respective database tables to video encoding software running on a processor.

Abstract: A system includes a processing device to receive video content, metadata related to the video content, and a target bit rate for encoding the video content. The processing device further detects a content type of the video content based on the metadata and encodes hardware to perform frame encoding on the video content. The system further includes a controller coupled between the processing device and the encoding hardware. The controller is programmed with machine instructions to generate first QP values on a per-frame basis using a frame machine learning model with a first plurality of weights. The first plurality of weights depends at least in part on the content type and the target bit rate. The controller further provides the first QP values to the encoding hardware for rate control of the frame encoding.

Abstract: Systems and methods herein are for a video encoder to be associated with a rate optimization distortion (RDO) module and a calibration module, where the RDO module may be to perform RDO for received frames of a media stream and may be to generate at least an RDO output that is based in part on quality measures between the received frames and decoded frames, and where the calibration module may be to provide an evaluation metric that is to scale or transform at least a range of the quality measures, with the scaling or transforming to potentially reduce an effect on a compression performed in the video encoder.

Abstract: A video coding system including an acceleration device including input circuitry configured, for each of a first plurality of video frames to be encoded, to receive an input including at least one raw video frame and at least one reference frame, and to divide each of the first plurality of video frames to be encoded into a second plurality of blocks, and similarity computation circuitry configured, for each one of the first plurality of video frame to be encoded: for each the block of the second plurality of blocks, to produce an intra-prediction hint and an intra-prediction direction. Related apparatus and methods are also provided.

Abstract: A system includes a processing device to receive a video content, a quality metric, and a target bit rate for encoding the video content. The system includes encoding hardware to perform frame encoding on the video content and a controller coupled between the processing device and the encoding hardware. The controller is programmed with machine instructions to generate first QP values on a per-frame basis using a frame machine learning model with a first plurality of weights. The first plurality of weights depends at least in part on the quality metric and the target bit rate. The controller is further programmed to provide the first QP values to the encoding hardware for rate control of the frame encoding.

Abstract: A video coding system including an acceleration device including input circuitry configured, for each of a first plurality of video frames to be encoded, to receive an input including at least one raw video frame and at least one reference frame, and to divide each of the first plurality of video frames to be encoded into a second plurality of blocks, and similarity computation circuitry configured, for each one of the first plurality of video frame to be encoded: for each the block of the second plurality of blocks, to produce an intra-prediction hint and an intra-prediction direction. Related apparatus and methods are also provided.

Abstract: Systems and methods herein are for a video encoder to be associated with an interface that is to receive, from an application, at least one metric that is associated with a quality preference for video compression to be performed by the video encoder and that is to provide a weight map to enable the video encoder to perform rate-distortion optimization (RDO) for received frames from the application using the weight map to weigh one or more first blocks associated with an individual one of the frames more than one or more second blocks associated with the individual one of the frames.

Abstract: A system includes a processing device to receive video content and output encoded video of the video content for a client video device. The system includes a controller coupled to the processing device, the controller programmed with machine instructions to receive, from a video encoder while encoding the video content, frame statistics based on one or more encoded frames of the video content corresponding to a current frame. The machine instructions further generate a first quantization parameter (QP) value for the current frame using a frame machine learning model, wherein the frame machine learning model includes states that depend on the frame statistics. The machine instructions further provide the first QP value to the video encoder for rate control of the frame encoding of the current frame.

Abstract: A system includes a processing device to receive video content, metadata related to the video content, and a target bit rate for encoding the video content. The processing device further detects a content type of the video content based on the metadata and encodes hardware to perform frame encoding on the video content. The system further includes a controller coupled between the processing device and the encoding hardware. The controller is programmed with machine instructions to generate first QP values on a per-frame basis using a frame machine learning model with a first plurality of weights. The first plurality of weights depends at least in part on the content type and the target bit rate. The controller further provides the first QP values to the encoding hardware for rate control of the frame encoding.

Abstract: A system includes a processing device to receive a video content, a quality metric, and a target bit rate for encoding the video content. The system includes encoding hardware to perform frame encoding on the video content and a controller coupled between the processing device and the encoding hardware. The controller is programmed with machine instructions to generate first QP values on a per-frame basis using a frame machine learning model with a first plurality of weights. The first plurality of weights depends at least in part on the quality metric and the target bit rate. The controller is further programmed to provide the first QP values to the encoding hardware for rate control of the frame encoding.

Abstract: In one embodiment, a system includes a hardware accelerator to receive video data of multiple video frames, divide each of the video frames into respective blocks, compute encoding assist data including at least one video encoding parameter type for each of the respective blocks of each of the video frames, and store respective portions of the encoding assist data across respective database tables, and an interface to provide the respective database tables to video encoding software running on a processor.

Abstract: A video processor includes a memory and a processor. The processor is coupled to memory and is configured to store in the memory (i) multiple raw frames belonging to a Group of Pictures (GOP) to be processed, and (ii) one or more reference frames. The processor is further configured to select for multiple target blocks having a same block-location in respective raw frames associated with a common reference frame, a common search region in the common reference frame, and before selecting another search region, to apply at least two motion estimation operations using at least two of the target blocks and the common search region, to estimate respective at least two Motion Vectors (MVs).

Abstract: A video processor includes a memory and a processor. The processor is coupled to memory and is configured to store in the memory (i) multiple raw frames belonging to a Group of Pictures (GOP) to be processed, and (ii) one or more reference frames. The processor is further configured to select for multiple target blocks having a same block-location in respective raw frames associated with a common reference frame, a common search region in the common reference frame, and before selecting another search region, to apply at least two motion estimation operations using at least two of the target blocks and the common search region, to estimate respective at least two Motion Vectors (MVs).

Abstract: A video coding system including an acceleration device including input circuitry configured, for each of a first plurality of video frames to be encoded, to receive an input including at least one raw video frame and at least one reference frame, and to divide each of the first plurality of video frames to be encoded into a second plurality of blocks, and similarity computation circuitry configured, for each one of the first plurality of video frame to be encoded: for each the block of the second plurality of blocks, to produce an intra-prediction hint and an intra-prediction direction. Related apparatus and methods are also provided.

Abstract: Described embodiments include a system that includes one or more buffers and circuitry. The circuitry is configured to process a plurality of input values, by identifying each of the input values that is not zero-valued, and, for each value of the identified input values, computing respective products of coefficients of a kernel with the value and storing at least some of the respective products in the buffers. The circuitry is further configured to compute a plurality of output values, by retrieving respective sets of stored values from the buffers, at least some of the retrieved sets including one or more of the products, and summing the retrieved sets. The circuitry is further configured to output the computed output values. Other embodiments are also described.

Abstract: Described embodiments include a system that includes one or more buffers and circuitry. The circuitry is configured to process a plurality of input values, by identifying each of the input values that is not zero-valued, and, for each value of the identified input values, computing respective products of coefficients of a kernel with the value and storing at least some of the respective products in the buffers. The circuitry is further configured to compute a plurality of output values, by retrieving respective sets of stored values from the buffers, at least some of the retrieved sets including one or more of the products, and summing the retrieved sets. The circuitry is further configured to output the computed output values. Other embodiments are also described.