Ultra Light Models and Decision Fusion for Fast Video Coding

    公开(公告)号:US20230007284A1

    公开(公告)日:2023-01-05

    申请号:US17779380

    申请日:2019-12-23

    Applicant: Google LLC

    Abstract: Ultra light models and decision fusion for increasing the speed of intra-prediction are described. Using a machine-learning (ML) model, an ML intra-prediction mode is obtained. A most-probable intra-prediction mode is obtained from amongst available intra-prediction modes for encoding the current block. As an encoding intra-prediction mode, one of the ML intra-prediction mode or the most-probable intra-prediction mode is selected, and the encoding intra-prediction mode is encoded in a compressed bitstream. A current block is encoded using the encoding intra-prediction mode. Selection of the encoding intra-prediction mode is based on relative reliabilities of the ML intra-prediction mode and the most-probable intra-prediction mode.

    Chroma transform type determination

    公开(公告)号:US12244803B2

    公开(公告)日:2025-03-04

    申请号:US18273666

    申请日:2021-01-25

    Applicant: Google LLC

    Abstract: For a coding block of an image, a luma prediction block is generated, a luma residual block is generated, a quantized luma block is generated after transforming the luma residual block using a luma transform type, and the quantized luma block is entropy encoded. A chroma prediction block is generated, a chroma residual block is generated, an initial chroma transform type for the chroma residual block is determined as the luma transform type, a quantized chroma block is generated using the chroma residual block transformed by a final chroma transform type, and the quantized chroma block is entropy encoded. When the initial chroma transform type is other than a default transform type, the final chroma transform type is the initial chroma transform type or the default transform type, and quantized coefficients of the quantized chroma block depend upon quantized coefficients of the quantized luma block.

    Regularization Of A Probability Model For Entropy Coding

    公开(公告)号:US20250150641A1

    公开(公告)日:2025-05-08

    申请号:US18836986

    申请日:2022-12-29

    Applicant: GOOGLE LLC

    Abstract: Entropy coding a sequence of syntax elements is described where an observation for a syntax element of the sequence is determined, and the observation is arithmetic coded using the probability model. Thereafter, the probability model is updated using a time-variant update rate to produce an updated probability model. Updating the probability model includes regularizing one or more probability values of the probability model so no probability of the updated probability model is below a defined minimum resolution. As a result, the use of a minimum probability value during the arithmetic coding, which can distort probability model, may be omitted.

    A MULTI-TRY ENCODING OPERATION FOR STREAMING APPLICATIONS

    公开(公告)号:US20250142069A1

    公开(公告)日:2025-05-01

    申请号:US18683383

    申请日:2021-09-03

    Applicant: GOOGLE LLC

    Abstract: A multi-try encoding operation is implemented to encode one or more game frames into a game stream. The multi-try encoding operation includes determining an initial quantization parameter for a current frame. From the determined initial quantization parameter, one or more alternative quantization parameters are derived. Multiple encoders then perform multiple encodings on the current frame based on the initial quantization parameter and the alternative quantization parameters, respectively, to produce a plurality of encoded frames. An applicable encoded frame is then selected from the plurality of encoded frames according to a streaming application. The applicable encoded frame is then transmitted as part of a game stream to a client system.

    Ultra light models and decision fusion for fast video coding

    公开(公告)号:US12225221B2

    公开(公告)日:2025-02-11

    申请号:US17779380

    申请日:2019-12-23

    Applicant: Google LLC

    Abstract: Ultra light models and decision fusion for increasing the speed of intra-prediction are described. Using a machine-learning (ML) model, an ML intra-prediction mode is obtained. A most-probable intra-prediction mode is obtained from amongst available intra-prediction modes for encoding the current block. As an encoding intra-prediction mode, one of the ML intra-prediction mode or the most-probable intra-prediction mode is selected, and the encoding intra-prediction mode is encoded in a compressed bitstream. A current block is encoded using the encoding intra-prediction mode. Selection of the encoding intra-prediction mode is based on relative reliabilities of the ML intra-prediction mode and the most-probable intra-prediction mode.

    ENTROPY CODING USING PRE-DEFINED, FIXED CDFS

    公开(公告)号:US20250088635A1

    公开(公告)日:2025-03-13

    申请号:US18803186

    申请日:2024-08-13

    Applicant: GOOGLE LLC

    Abstract: Entropy coding a sequence of transform coefficients includes determining a predictor value corresponding to a transform coefficient, selecting a probability model from a set of pre-defined probability models based on the predictor value, and entropy coding a symbol associated with the transform coefficient using the selected probability model. The predictor value can be calculated based on a previous predictor value used for coding an immediately preceding symbol associated with an immediately preceding transform coefficient of the sequence of the transform coefficients. The predictor value can be further calculated based on the immediately preceding symbol.

    HARDWARE EFFICIENT DECODER SIDE MOTION VECTOR REFINEMENT

    公开(公告)号:US20250016340A1

    公开(公告)日:2025-01-09

    申请号:US18766519

    申请日:2024-07-08

    Applicant: GOOGLE LLC

    Abstract: Various hardware arrangements for use with a hardware decoder are described to perform motion vector (MV) refinement. A buffer stores pixel values for a first prediction block identified by a first initial MV and pixels defined by a first search area and stores a second prediction block identified by a second initial MV and pixels defined by a second search area. The first and second initial MVs were used for inter prediction of a block of image data. Second hardware components determine, for multiple pixel locations defined by respective offsets, a respective difference value between the blocks shifted by the offsets. A third hardware component determines a minimum of the difference values to identify an offset MV for MV refinement, and a fourth hardware component outputs one or more refined values based on the offset MV to reconstruct a sub-block of the block.

    Overlapped Filtering For Temporally Interpolated Prediction Blocks

    公开(公告)号:US20250142050A1

    公开(公告)日:2025-05-01

    申请号:US18927278

    申请日:2024-10-25

    Applicant: Google LLC

    Abstract: Filtering an interpolated reference frame is described. The interpolated reference frame is generated by determining, from a motion field, a motion vector pointing towards a forward reference frame and a motion vector pointing towards a backward reference frame. Expanded prediction blocks, compared to the size of the block of the interpolated reference frame, are determined using the motion vectors and reference frames. The expanded prediction blocks form overlapping areas with adjacent blocks of the interpolated reference frame. The overlapping areas are filtered to mitigate discontinuities.

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