公开(公告)号：US10515092B2

公开(公告)日：2019-12-24

申请号：US15656485

申请日：2017-07-21

Applicant: Google LLC

Inventor： Marcin Kowalczyk ,  Robert Obryk ,  Jyrki Alakuijala ,  Alkis Evlogimenos ,  Jan Wassenberg ,  Tomas Dzetkulic

IPC: G06F17/30 ,  G06F16/25 ,  G06F16/22

Abstract: This technology relates to encoding data. For example, a sequence of one or more structured records as input data, at least one of the structured records including one or more field tags and associated field data. The input data may be parsed into data buffers, each data buffer corresponding to a field tag in the one or more field tags, wherein each data buffer includes the associated field data of the corresponding field tag. A control sequence specifying a sequence of the one or more fields tags may be encoded into a transition record. A state machine comprising nodes and transitions may be generated, with each node corresponding to occurrences of the one or more field tags and each transition corresponding to successive pairs of the one or more field tags. The data buffers, a representation of the state machine, and the encoded control sequence may be output.

公开(公告)号：US20230016253A1

公开(公告)日：2023-01-19

申请号：US17955788

申请日：2022-09-29

Applicant: GOOGLE LLC

Inventor： Jyrki Alakuijala ,  Robert Obryk ,  Evgenii Kliuchnikov ,  Zoltan Szabadka ,  Jan Wassenberg ,  Minttu Alakuijala ,  Lode Vandevenne

IPC: H04N19/65 ,  H04N19/124 ,  H04N19/18 ,  H04N19/184 ,  H04N19/85

Abstract: The loss of image quality during compression is controlled using a sequence of quality control metrics. The sequence of quality control metrics is selected for quantizing transform coefficients within an area of the image based on an error level definition. Candidate bit costs are then determined by quantizing the transform coefficients according to the error level definition or a modified error level and the sequence of quality control metrics. Where the candidate bit cost resulting from using the modified error level is lower than the candidate bit cost resulting from using the error level definition, the transform coefficients are quantized according to the modified error level and the sequence of quality control metrics. Otherwise, the transform coefficients are quantized based on the error level definition and according to the sequence of quality control metrics.

公开(公告)号：US20210195193A1

公开(公告)日：2021-06-24

申请号：US17248795

申请日：2021-02-08

Applicant: Google LLC

Inventor： Krzysztof Potempa ,  Jyrki Alakuijala ,  Robert Obryk

IPC: H04N19/122 ,  H04N19/176 ,  H04N19/625

Abstract: An image encoder includes a processor and a memory. The memory includes instructions configured to cause the processor to perform operations. In one example implementation, the operations may include determining whether a dictionary item is available for replacing a block of an image being encoded, the determining based on a hierarchical lookup mechanism, and encoding the image along with reference information of the dictionary item in response to determining that the dictionary item is available. In one more example implementation, the operations may include performing principal component analysis (PCA) on a block to generate a corresponding projected block, the block being associated with a group of images, comparing the projected block with a corresponding threshold, descending the block recursively based on the threshold until a condition is satisfied, and identifying a left over block as a cluster upon satisfying of the condition.

公开(公告)号：US20210084339A1

公开(公告)日：2021-03-18

申请号：US16970499

申请日：2019-02-15

Applicant: Google LLC

Inventor： Jyrki Alakuijala ,  Robert Obryk ,  Evgenii Kliuchnikov ,  Zoltan Szabadka ,  Jan Wassenberg ,  Minttu Alakuijala ,  Lode Vandevenne

IPC: H04N19/65 ,  H04N19/124 ,  H04N19/18 ,  H04N19/184 ,  H04N19/85

Abstract: The loss of image quality during compression is controlled using a sequence of quality control metrics. The sequence of quality control metrics is selected for quantizing transform coefficients within an area of the image based on an error level definition. Candidate bit costs are then determined by quantizing the transform coefficients according to the error level definition or a modified error level and the sequence of quality control metrics. Where the candidate bit cost resulting from using the modified error level is lower than the candidate bit cost resulting from using the error level definition, the transform coefficients are quantized according to the modified error level and the sequence of quality control metrics. Otherwise, the transform coefficients are quantized based on the error level definition and according to the sequence of quality control metrics.

公开(公告)号：US10931948B2

公开(公告)日：2021-02-23

申请号：US15985317

申请日：2018-05-21

Applicant: Google LLC

Inventor： Krzysztof Potempa ,  Jyrki Alakuijala ,  Robert Obryk

IPC: H04N19/122 ,  H04N19/176 ,  H04N19/625

Abstract: An image encoder includes a processor and a memory. The memory includes instructions configured to cause the processor to perform operations. In one example implementation, the operations may include determining whether a dictionary item is available for replacing a block of an image being encoded, the determining based on a hierarchical lookup mechanism, and encoding the image along with reference information of the dictionary item in response to determining that the dictionary item is available. In one more example implementation, the operations may include performing principal component analysis (PCA) on a block to generate a corresponding projected block, the block being associated with a group of images, comparing the projected block with a corresponding threshold, descending the block recursively based on the threshold until a condition is satisfied, and identifying a left over block as a cluster upon satisfying of the condition.

公开(公告)号：US20190251444A1

公开(公告)日：2019-08-15

申请号：US16274599

申请日：2019-02-13

Applicant: Google LLC

Inventor： Jyrki Alakuijala ,  Ruud van Asseldonk ,  Robert Obryk ,  Krzysztof Potempa

IPC: G06N3/08 ,  G06N3/04 ,  G06N20/20

CPC classification number: G06N3/082 ,  G06N3/0454 ,  G06N3/084 ,  G06N20/20

Abstract: The present disclosure provides systems and methods for modification (e.g., pruning, compression, quantization, etc.) of artificial neural networks based on estimations of the utility of network connections (also known as “edges”). In particular, the present disclosure provides novel techniques for estimating the utility of one or more edges of a neural network in a fashion that requires far less expenditure of resources than calculation of the actual utility. Based on these estimated edge utilities, a computing system can make intelligent decisions regarding network pruning, network quantization, or other modifications to a neural network. In particular, these modifications can reduce resource requirements associated with the neural network. By making these decisions with knowledge of and based on the utility of various edges, this reduction in resource requirements can be achieved with only a minimal, if any, degradation of network performance (e.g., prediction accuracy).