Abstract: An example disclosed system may include a hardware video encoding pipeline (HVEP), the HVEP that includes a prediction module that generates, using a primary prediction mode, a primary encode of a portion of a video stream, and using a secondary prediction mode, a secondary encode of the portion of the video stream. The HVEP may also include (1) a rate-distortion optimization (RDO) module that determines a primary cost associated with the primary encode and a secondary cost associated with the secondary encode, (2) a quality metric (QM) module that determines a primary QM associated with the primary encode and a secondary QM associated with the secondary encode, and (3) a decision module that selects, for an encoding of the video stream, at least one of the primary prediction mode and the secondary prediction mode based on the primary cost, the secondary cost, the primary QM, and the secondary QM.

Abstract: A disclosed system may include a hardware distortion data pipeline that may include (1) a quantization module that generates a quantized data set, (2) an inverse quantization module that generates, from the quantized data set, an inverse quantized data set by executing an inverse quantization of the quantized data set, and (3) an inverse transformation module that generates an inversely transformed data set by executing an inverse transformation of the inverse quantized data set. The system may also include a hardware determination pipeline that determines a distortion metric based on the inversely transformed data set and the residual frame data set, and a hardware token rate pipeline that determines, based on the quantized data set, a token rate for an encoding of the residual frame data set via a video encoding pipeline. Various other methods, systems, and computer-readable media are also disclosed.

Abstract: An example system may include a primary mode decision module, included in a hardware video encoding pipeline, that (1) receives video data for encoding in accordance with a video encoding standard, and (2) identifies, from an initial set of prediction modes supported by the video encoding standard, a primary set of prediction modes for encoding the video data in accordance with the video encoding standard. The example system may also include a secondary mode decision module that (1) determines, for each prediction mode included in the primary set of prediction modes and based on the video data, a cost associated with the prediction mode, and (2) selects, from the primary set of prediction modes and based on the determined costs associated with the prediction modes included in the primary set of prediction modes, a prediction mode for encoding of the video data by the hardware video encoding pipeline.

Abstract: An example system may include a primary mode decision module, included in a hardware video encoding pipeline, that (1) receives video data for encoding in accordance with a video encoding standard, and (2) identifies, from an initial set of prediction modes supported by the video encoding standard, a primary set of prediction modes for encoding the video data in accordance with the video encoding standard. The example system may also include a secondary mode decision module that (1) determines, for each prediction mode included in the primary set of prediction modes and based on the video data, a cost associated with the prediction mode, and (2) selects, from the primary set of prediction modes and based on the determined costs associated with the prediction modes included in the primary set of prediction modes, a prediction mode for encoding of the video data by the hardware video encoding pipeline.

Abstract: A disclosed method may include storing, within a hardware memory device included as part of a rate—distortion optimization (RDO) hardware pipeline, at least one transform unit table that (1) is pregenerated from a seed probability table for transformation of video data in accordance with a video encoding standard, (2) corresponds to a transform operation supported by the video encoding standard, and (3) corresponds to a transform unit included in the RDO hardware pipeline. The method may also include determining, by accessing the transform unit table, an RDO token rate for an encoding of the video data by a hardware video encoding pipeline that includes the RDO hardware pipeline, and selecting, based on the RDO token rate, a transform operation for the encoding of the video data.

Abstract: A disclosed system may include a hardware distortion data pipeline that may include (1) a quantization module that generates a quantized data set, (2) an inverse quantization module that generates, from the quantized data set, an inverse quantized data set by executing an inverse quantization of the quantized data set, and (3) an inverse transformation module that generates an inversely transformed data set by executing an inverse transformation of the inverse quantized data set. The system may also include a hardware determination pipeline that determines a distortion metric based on the inversely transformed data set and the residual frame data set, and a hardware token rate pipeline that determines, based on the quantized data set, a token rate for an encoding of the residual frame data set via a video encoding pipeline. Various other methods, systems, and computer-readable media are also disclosed.

Abstract: A mode decision component is configured to determine the costs of different modes for a selected partition of a frame of a video using an estimated compression coding data, which is calculated prior to a corresponding actual compression coding data being calculated based on another partition immediately prior to the selected partition in a partition processing order. The estimated compression coding data is determined based on a previously calculated compression coding data calculated based on a completed partition prior to the selected partition in the partition processing order. The mode decision component is configured to use the determined costs to select one of the modes. An encoder component is configured to use the selected mode to encode the selected partition by using the corresponding actual compression coding data calculated based on the another partition immediately prior to the selected partition in the partition processing order.

Abstract: A method for sharing the motion estimation and mode decision results and decisions of one codec with another codec is disclosed. A video is received to be transcoded into a plurality of different output encodings of a plurality of different codecs. Each codec has a different video encoding format. A shared motion estimation and a shared mode decision processing of the video are performed. One or more results of the shared mode decision processing shared across the plurality of different codecs are used to encode the video into the plurality of different output encodings of the plurality of different codecs.

Abstract: A quantized transform coefficient matrix is partitioned into a sequence of partition portions. The coefficients of the matrix are grouped into the sequence of partition portions based on a hardware implemented scan order. Each partition portion is processed in an order of the sequence in a first pass. For each partition portion, a group of coefficients in the partition portion is determined. For each partition portion, a first data rate estimation for the quantized transform coefficient matrix is updated based on at least some coefficients of the group of coefficients in the partition portion and a maximum end-of-block. For each partition portion, an end-of-block estimation of the quantized transform coefficient matrix is updated based on at least some coefficients of the group of coefficients in the partition portion. A first resulting data rate estimation and a true end-of-block of the quantized transform coefficient matrix are determined after the first pass.

Abstract: The disclosed computer-implemented method may include downsampling and encoding one or more video segments into a plurality of encoded segments with an analysis encoder using a plurality of encoding parameter value sets and decoding and upsampling the plurality of encoded segments to a plurality of decoded segments at an original resolution of the one or more video segments. The method may further include determining, based on analyzing the plurality of decoded segments, an analysis encoding parameter value set for the analysis encoder for the one or more video segments and predicting, based on the analysis encoding parameter value set, a target encoding parameter value set for a target encoder for the one or more video segments. The method may also include encoding the one or more video segments with the target encoder using the target encoding parameter value set. Various other methods, systems, and computer-readable media are also disclosed.

Abstract: A system for calculating token rates for video encoding includes a plurality of different probability lookup tables implemented in hardware, wherein each of the probability lookup tables specifically corresponds to a different prediction mode of a video codec. The system includes an application-specific integrated circuit compute unit. For each candidate prediction mode among the different prediction modes, the application-specific integrated circuit is configured to determine a rate distortion cost (RD Cost) for a video. The application-specific integrated circuit is configured to select one of the plurality of different probability lookup tables that corresponds to the candidate prediction mode and use the selected one of the plurality of different probability lookup tables to calculate a corresponding token rate for the candidate prediction mode.

Abstract: A method for sharing the motion estimation and mode decision results and decisions of one codec with another codec is disclosed. A video is received to be transcoded into a plurality of different output encodings of a plurality of different codecs. Each codec has a different video encoding format. A shared motion estimation and a shared mode decision processing of the video are performed. One or more results of the shared mode decision processing shared across the plurality of different codecs are used to encode the video into the plurality of different output encodings of the plurality of different codecs.

Abstract: A video to be encoded using a codec is received. A first-pass analysis of the frames of the video is performed, including by collecting first-pass statistics data for each of the frames of the video. A specific frame of the video is selected for boosting an encoding rate of the specific frame. At least a portion of the first-pass statistics data is provided to a model to determine a boost factor for the specific frame. The encoding rate for the specific frame is determined using the boost factor.

Abstract: A hardware video processor comprises a cost calculation unit. The cost calculation unit is configured to determine rate distortion costs of a plurality of different modes for a portion of a video. The hardware video processor further comprises an evaluation unit. The evaluation unit is configured to receive the rate distortion costs of the plurality of different modes. At least one component of at least one of the rate distortion costs is adjusted based on a condition to determine at least one modified rate distortion cost of at least one of the plurality of different modes. The at least one modified rate distortion cost is used to evaluate the plurality of different modes and select one of the modes for use in encoding the portion of the video.

Abstract: A quantized transform coefficient matrix is partitioned into a sequence of partition portions. The coefficients of the matrix are grouped into the sequence of partition portions based on a hardware implemented scan order. Each partition portion is processed in an order of the sequence in a first pass. For each partition portion, a group of coefficients in the partition portion is determined. For each partition portion, a first data rate estimation for the quantized transform coefficient matrix is updated based on at least some coefficients of the group of coefficients in the partition portion and a maximum end-of-block. For each partition portion, an end-of-block estimation of the quantized transform coefficient matrix is updated based on at least some coefficients of the group of coefficients in the partition portion. A first resulting data rate estimation and a true end-of-block of the quantized transform coefficient matrix are determined after the first pass.

Abstract: A method for sharing the motion estimation and mode decision results and decisions of one codec with another codec is disclosed. A video is received to be transcoded into a plurality of different output encodings of a plurality of different codecs. Each codec has a different video encoding format. A shared motion estimation and a shared mode decision processing of the video are performed. One or more results of the shared mode decision processing shared across the plurality of different codecs are used to encode the video into the plurality of different output encodings of the plurality of different codecs.

Abstract: The disclosed may include various systems and methods for improving the efficiency and scalability of large-scale systems. For example, the disclosed may include systems and methods for automatic privacy enforcement using privacy-aware infrastructure, scalable general-purpose low cost integer motion search, efficient scaler filter coefficients layout for flexible scaling quality control with limited hardware resources, hardware optimization for power saving with both different codecs enabled, optimizing storage overhead and performance for large distributed data warehouse, mass and volume efficient integration of intersatellite link terminals to a satellite bus, and overcoming retention limit for memory-based distributed database systems.

Abstract: A method and apparatus for determining neighborhood video attribute values for a frame of video data are disclosed. In one aspect, the method includes receiving coordinates for a current block location and a previous block location of the frame and generating a vector indicative of the displacement of the current block from the previous block via comparing the coordinates of the current block location to the coordinates of the previous block location. The method may also include updating the neighborhood video attribute values based at least in part on the generated vector and processing the frame based on the updated neighborhood video attribute values.

Abstract: A method and apparatus for determining neighborhood video attribute values for a frame of video data are disclosed. In one aspect, the method includes receiving coordinates for a current block location and a previous block location of the frame and generating a vector indicative of the displacement of the current block from the previous block via comparing the coordinates of the current block location to the coordinates of the previous block location. The method may also include updating the neighborhood video attribute values based at least in part on the generated vector and processing the frame based on the updated neighborhood video attribute values.