Invention Application
- Patent Title: SUBSTITUTIONAL QUALITY FACTOR LEARNING FOR QUALITY-ADAPTIVE NEURAL NETWORK-BASED LOOP FILTER
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Application No.: PCT/US2022/029122Application Date: 2022-05-13
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Publication No.: WO2022245640A2Publication Date: 2022-11-24
- Inventor: JIANG, Wei , WANG, Wei , XU, Xiaozhong , LIU, Shan
- Applicant: TENCENT AMERICA LLC
- Applicant Address: 2747 Park Boulevard
- Assignee: TENCENT AMERICA LLC
- Current Assignee: TENCENT AMERICA LLC
- Current Assignee Address: 2747 Park Boulevard
- Agency: RABENA, John F. et al.
- Priority: US17/741,703 2022-05-11
- Main IPC: H04N19/154
- IPC: H04N19/154 ; H04N19/117 ; G06N3/08 ; H04N19/172 ; G06N3/084 ; G06T2207/10016 ; G06T2207/20081 ; G06T2207/20084 ; G06T5/001 ; G06T5/002 ; G06T9/002 ; H04N19/134 ; H04N19/139 ; H04N19/159 ; H04N19/176 ; H04N19/82
Abstract:
A method, apparatus, and non-transitory computer-readable medium for adaptive neural image compression by meta-learning using substitute QF settings, which includes generating one or more substitute quality factors via a plurality of iterations using the original quality factors, wherein the substitute quality factors are a modified version of the original quality factors. The approach may further include determining a neural network based loop filter comprising neural network based loop filter parameters and a plurality of layers, wherein the neural network based loop filter parameters include shared parameters and adaptive parameters, and may further include generating enhanced video data, based on the one or more substitute quality factors and the input video data, using the neural network based loop filter.
Information query