公开(公告)号：US12219180B2

公开(公告)日：2025-02-04

申请号：US17749846

申请日：2022-05-20

Applicant: Adobe Inc.

Inventor： Gang Wu ,  Yang Li ,  Stefano Petrangeli ,  Viswanathan Swaminathan ,  Haoliang Wang ,  Ryan A. Rossi ,  Zhao Song

IPC: G06K9/00 ,  G06N20/00 ,  H04N19/182 ,  H04N19/184 ,  H04N19/50 ,  H04N19/91 ,  H04N19/96

Abstract: Embodiments described herein provide methods and systems for facilitating actively-learned context modeling. In one embodiment, a subset of data is selected from a training dataset corresponding with an image to be compressed, the subset of data corresponding with a subset of data of pixels of the image. A context model is generated using the selected subset of data. The context model is generally in the form of a decision tree having a set of leaf nodes. Entropy values corresponding with each leaf node of the set of leaf nodes are determined. Each entropy value indicates an extent of diversity of context associated with the corresponding leaf node. Additional data from the training dataset is selected based on the entropy values corresponding with the leaf nodes. The updated subset of data is used to generate an updated context model for use in performing compression of the image.

公开(公告)号：US20230379507A1

公开(公告)日：2023-11-23

申请号：US17749846

申请日：2022-05-20

Applicant: Adobe Inc.

Inventor： Gang Wu ,  Yang Li ,  Stefano Petrangeli ,  Viswanathan Swaminathan ,  Haoliang Wang ,  Ryan A. Rossi ,  Zhao Song

IPC: H04N19/96 ,  H04N19/91 ,  H04N19/50 ,  H04N19/184 ,  H04N19/182 ,  G06N20/00

CPC classification number: H04N19/96 ,  H04N19/91 ,  H04N19/50 ,  H04N19/184 ,  H04N19/182 ,  G06N20/00

Abstract: Embodiments described herein provide methods and systems for facilitating actively-learned context modeling. In one embodiment, a subset of data is selected from a training dataset corresponding with an image to be compressed, the subset of data corresponding with a subset of data of pixels of the image. A context model is generated using the selected subset of data. The context model is generally in the form of a decision tree having a set of leaf nodes. Entropy values corresponding with each leaf node of the set of leaf nodes are determined. Each entropy value indicates an extent of diversity of context associated with the corresponding leaf node. Additional data from the training dataset is selected based on the entropy values corresponding with the leaf nodes. The updated subset of data is used to generate an updated context model for use in performing compression of the image.