公开(公告)号：US20190191164A1

公开(公告)日：2019-06-20

申请号：US15880939

申请日：2018-01-26

Applicant: GOOGLE LLC

Inventor： Jingning Han ,  Dake He

IPC: H04N19/129 ,  H04N19/61 ,  H04N19/18 ,  H04N19/176 ,  H04N19/82

CPC classification number: H04N19/129 ,  H04N19/176 ,  H04N19/18 ,  H04N19/61 ,  H04N19/82

Abstract: A scan order for encoding or decoding coefficients of a transform block is selected on a transform block-level. A set of candidate scan orders is processed by identifying end of block positions within the transform block for each of the candidate scan orders. Cost values are determined for each of the candidate scan orders to reflect a number of the coefficients of the transform block that are located before the respective end of block positions. In particular, a cost value for a candidate scan order reflects the number of zero-value coefficients located before the end of block position for that candidate scan order. One of the candidate scan orders is then selected based on those cost values. The selected scan order is used to scan the coefficients in the transform block, such as for encoding those coefficients to a bitstream or for decoding those coefficients to an output video stream.

公开(公告)号：US20190124342A1

公开(公告)日：2019-04-25

申请号：US15798495

申请日：2017-10-31

Applicant: GOOGLE LLC

Inventor： Joseph Young ,  Dake He

IPC: H04N19/18 ,  H04N19/129 ,  H04N19/13 ,  H04N19/176

Abstract: Coding using level maps is disclosed. A method includes coding a scan position, in a forward scan direction, corresponding to an end-of-block and coding, in a backward scan direction, a non-zero map indicating positions of the transform block containing non-zero transform coefficients. The method also includes coding, in the backward scan direction, lower-range level maps, each lower-range level map having a respective map level up to a maximum map level, the lower-range level map indicating which absolute values of the non-zero transform coefficients are equal to the respective map level and which absolute values of the non-zero transform coefficients are greater than the respective map level. The method also includes coding a coefficient residual map, each residual coefficient of the coefficient residual map corresponding to a respective non-zero transform coefficient of the transform block having an absolute value exceeding the maximum map level.

公开(公告)号：US11956447B2

公开(公告)日：2024-04-09

申请号：US17601639

申请日：2019-03-21

Applicant: Google LLC

Inventor： Claudionor Coelho ,  Aki Kuusela ,  Joseph Young ,  Shan Li ,  Dake He

IPC: H04N19/147 ,  G06T9/00 ,  H04N19/176 ,  H04N19/96

CPC classification number: H04N19/147 ,  G06T9/002 ,  H04N19/176 ,  H04N19/96

Abstract: An apparatus for encoding an image block includes a processor that presents, to a machine-learning model, the image block, obtains the partition decision for encoding the image block from the model, and encodes the image block using the partition decision. The model is trained to output a partition decision for encoding the image block by using training data for a plurality of training blocks as input, the training data including for a training block, partition decisions for encoding the training block, and, for each partition decision, a rate-distortion value resulting from encoding the training block using the partition decision. The model is trained using a loss function combining a partition loss function based upon a relationship between the partition decisions and respective predicted partitions, and a rate-distortion cost loss function based upon a relationship between the rate-distortion values and respective predicted rate-distortion values.

公开(公告)号：US20240089177A1

公开(公告)日：2024-03-14

申请号：US18497571

申请日：2023-10-30

Applicant: Google LLC

Inventor： Oleg Golubitsky ,  Pushkarini Hemchandra Agharkar ,  Dake He

IPC: H04L41/142 ,  H04L41/12

CPC classification number: H04L41/142 ,  H04L41/12

Abstract: Systems and methods of enforcing policies in a computer environment for content distribution using pointwise mutual information (PMI) based clustering are provided. The system can maintain a network of nodes representing a plurality of assets. Upon detecting that an asset is associated with a policy label, the system can identify attributes of the asset and compute a PMI score indicating whether nodes of the network sharing the attributes belong to a single content source. Upon determining that the PMI score exceeds a predefined threshold value, the system can identify a cluster of nodes including the nodes sharing the attributes. The system can tag the cluster, for example, as being associated with a content source that is associated with the policy label.

公开(公告)号：US11917156B2

公开(公告)日：2024-02-27

申请号：US18084719

申请日：2022-12-20

Applicant: GOOGLE LLC

Inventor： Dake He

IPC: H04N19/129 ,  H04N19/18 ,  H04N19/172 ,  H04N19/176 ,  H04N19/182 ,  H04N19/61

CPC classification number: H04N19/129 ,  H04N19/172 ,  H04N19/176 ,  H04N19/18 ,  H04N19/182 ,  H04N19/61

Abstract: Decoding a current block includes decoding a subset of quantized transform coefficients of a quantized transform block using a first scan order. A second scan order is determined based on the subset of the quantized transform coefficients. Remaining quantized transform coefficients of the quantized transform block are decoded based on the second scan order. A context model for decoding an intra-prediction mode is determined based on at least the subset of the quantized transform coefficients. The intra-prediction mode is decoded based on the context model. The current block is obtained based on the quantized transform coefficients and the intra-prediction mode.

公开(公告)号：US20230080618A1

公开(公告)日：2023-03-16

申请号：US17799428

申请日：2020-02-24

Applicant: Google LLC

Inventor： Oleg Golubitsky ,  Pushkarini Hemchandra Agharkar ,  Dake He

IPC: H04L41/142 ,  H04L41/12

Abstract: Systems and methods of enforcing policies in a computer environment for content distribution using pointwise mutual information (PMI) based clustering are provided. The system can maintain a network of nodes representing a plurality of assets. Upon detecting that an asset is associated with a policy label, the system can identify attributes of the asset and compute a PMI score indicating whether nodes of the network sharing the attributes belong to a single content source. Upon determining that the PMI score exceeds a predefined threshold value, the system can identify a cluster of nodes including the nodes sharing the attributes. The system can tag the cluster, for example, as being associated with a content source that is associated with the policy label.

公开(公告)号：US11310498B2

公开(公告)日：2022-04-19

申请号：US17086591

申请日：2020-11-02

Applicant: GOOGLE LLC

Inventor： Claudionor Coelho ,  Aki Kuusela ,  Shan Li ,  Dake He

IPC: H04N19/119 ,  H04N19/19 ,  H04N19/147 ,  H04N19/176

Abstract: An apparatus for encoding a block of a picture includes a convolutional neural network (CNN) for determining a block partitioning of the block, the block having an N×N size and a smallest partition determined by the CNN being of size S×S. The CNN includes feature extraction layers; a concatenation layer that receives, from the feature extraction layers, first feature maps of the block, where each first feature map of the first feature maps is of the smallest possible partition size S×S of the block; and at least one classifier that is configured to infer partition decisions for sub-blocks of size (αS)×(αS) of the block, where α is a power of 2.

公开(公告)号：US20220094938A1

公开(公告)日：2022-03-24

申请号：US17544244

申请日：2021-12-07

Applicant: GOOGLE LLC

Inventor： Dake He

IPC: H04N19/129 ,  H04N19/18 ,  H04N19/172 ,  H04N19/176 ,  H04N19/182 ,  H04N19/61

Abstract: Decoding a transform block includes decoding a first group of coefficients of the transform block using a first scan order. The first group includes of first coefficients of a first row along a first edge of the transform block and second coefficients of a first column that is along a second edge of the transform block. The first group is used to determine a second scan order for decoding a second group of coefficients of the transform block. The second group includes remaining coefficients of the transform block and does not include any coefficient of the first group. The second group is decoded using the second scan order.

公开(公告)号：US20210051322A1

公开(公告)日：2021-02-18

申请号：US17086591

申请日：2020-11-02

Applicant: GOOGLE LLC

Inventor： Claudionor Coelho ,  Aki Kuusela ,  Shan Li ,  Dake He

IPC: H04N19/119 ,  H04N19/19 ,  H04N19/147 ,  H04N19/176

Abstract: An apparatus for encoding a block of a picture includes a convolutional neural network (CNN) for determining a block partitioning of the block, the block having an N×N size and a smallest partition determined by the CNN being of size S×S. The CNN includes feature extraction layers; a concatenation layer that receives, from the feature extraction layers, first feature maps of the block, where each first feature map of the first feature maps is of the smallest possible partition size S×S of the block; and at least one classifier that is configured to infer partition decisions for sub-blocks of size (αS)×(αS) of the block, where α is a power of 2.

公开(公告)号：US10834410B2

公开(公告)日：2020-11-10

申请号：US16580226

申请日：2019-09-24

Applicant: GOOGLE LLC

Inventor： Joseph Young ,  Dake He

IPC: H04N19/18 ,  H04N19/176 ,  H04N19/13 ,  H04N19/129 ,  H04N19/91 ,  H04N19/93 ,  H04N19/147 ,  H04N19/184 ,  H04N19/124 ,  H04N19/159 ,  H04N19/44 ,  H04N19/122

Abstract: A method for coding a current block using an intra-prediction mode includes defining a mapping from available intra-prediction modes to intra-prediction classes; determining, using the mapping, a first intra-prediction class of a first intra-prediction mode used for decoding a first neighboring block of the current block; determining, using the mapping, a second intra-prediction class of a second intra-prediction mode used for decoding a second neighboring block of the current block; using the first intra-prediction class and the second intra-prediction class as indices into a list of available context models to select a context model for coding the intra-prediction mode; and coding the intra-prediction mode using the context model. A first number of the intra-prediction classes is smaller than a second number of the available intra-prediction modes. each class is an ordinal value, and each available intra-prediction mode uniquely maps to one class of the intra-prediction classes.