US 12,407,811 B2
DNN-based cross component prediction
Sheng Lin, San Jose, CA (US); Wei Jiang, Sunnyvale, CA (US); Wei Wang, Palo Alto, CA (US); Liqiang Wang, Palo Alto, CA (US); Shan Liu, San Jose, CA (US); and Xiaozhong Xu, State College, PA (US)
Assigned to TENCENT AMERICA LLC, Palo Alto, CA (US)
Filed by TENCENT AMERICA LLC, Palo Alto, CA (US)
Filed on Jan. 16, 2024, as Appl. No. 18/413,713.
Application 18/413,713 is a continuation of application No. 17/749,730, filed on May 20, 2022, granted, now 11,909,956.
Claims priority of provisional application 63/210,741, filed on Jun. 15, 2021.
Prior Publication US 2024/0155112 A1, May 9, 2024
This patent is subject to a terminal disclaimer.
Int. Cl. H04N 19/105 (2014.01); H04N 19/132 (2014.01); H04N 19/176 (2014.01); H04N 19/186 (2014.01)
CPC H04N 19/105 (2014.11) [H04N 19/132 (2014.11); H04N 19/176 (2014.11); H04N 19/186 (2014.11)] 20 Claims
OG exemplary drawing
 
11. A system comprising:
at least one memory configured to store computer program code; and
at least one processor configured to access the computer program code and operate as instructed by the computer program code, the computer program code comprising:
obtaining code configured to cause the at least one processor to obtain reference components and side information associated with a reconstructed luma block, the side information includes block partition depth information; and
predicting code configured to cause the at least one processor to predict, by a deep neural network (DNN) that is implemented by the at least one processor, a reconstructed chroma block based on the reconstructed luma block, the reference components, and the side information that are input.