Meta Platforms, Inc.

Gaurang Chaudhari

Harikrishna Madadi Reddy

Yunqing Chen

Zhao Wang

Junqiang Lan

Hariharan Ganesh Lalgudi

Baheerathan Anandharengan

Srikanth Alaparthi

Prahlad Rao Venkatapuram

Ioannis Katsavounidis

Ping-Hao Wu

Volodymyr Kondratenko

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.

Systems and methods for improving mode decisions in hardware video encoders

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.

Hardware pipelines for rate-distortion optimization (RDO) that support multiple codecs

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.

Systems and methods for dynamic early termination of mode decision in hardware video encoders

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.

Architecture to adapt cumulative distribution functions for mode decision in video encoding

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.

Predicting encoding parameters for convex hull video encoding

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.

Cross-codec encoding optimizations for video transcoding

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. The application-specific integrated circuit is configured to encode the video using a selected one of the different prediction modes determined based on the determined rate distortion costs.

Hardware optimization of rate calculation in rate distortion optimization for video encoding

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.

Growing community of inventors

Sunnyvale, CA, United States of America

Gaurang Chaudhari