Video Coding Prediction Using Multiple GLMs and Reference Lines
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Solution Overview
Problem
Existing video coding technologies, such as MPEG-2, MPEG-4, ITU-T.263, ITU-T.264/MPEG-4 AVC, and ITU-T.265 HEVC, require improvements in coding efficiency and effectiveness.
Innovation Solution
Implementing gradient linear models (GLMs) for video coding, including determining multiple GLMs for video units and decoupling the number of reference lines from convolutional cross-component model (CCCM) modes, to enhance coding efficiency and effectiveness.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If multiple gradient linear models (GLMs) are determined for video coding, then coding efficiency is improved, but device complexity increases
Solution Approach 1:
The current video unit is divided into multiple regions, each with its own GLM model. By segmenting the video unit and applying different GLM models to different segments, the patent achieves better coding efficiency while managing model complexity through localized rather than global modeling.
Solution Approach 2:
The patent determines multiple GLM models with different parameters for different regions of the video unit. By changing model parameters adaptively across different segments rather than using a single uniform model, coding efficiency is improved while the complexity is managed through parameter variation rather than structural complexity.
2Reliability
If the number of reference lines is decoupled from CCCM mode, then coding effectiveness is improved, but processing complexity increases
Solution Approach 1:
The patent dynamically determines the number of reference lines based on the specific coding mode and content characteristics rather than using a fixed number. This dynamic adaptation improves coding effectiveness by optimizing reference line usage for each scenario while managing processing complexity through adaptive rather than rigid processing.
Solution Approach 2:
By decoupling the number of reference lines from the CCCM mode, the patent allows independent optimization of reference line parameters. This enables the system to adjust the number of reference lines as a separate parameter based on actual coding needs, improving effectiveness while managing complexity through parameter independence.
Data Source
AI summary
Embodiments of the present disclosure provide a solution for video processing. A method for video processing is proposed. In the method, for a conversion between a current video unit of a video and a bitstream of the video, a plurality of gradient linear models (GLMs) for the current video unit is determined. The current video unit comprises a GLM mode coded chroma block. A prediction of the current video unit is determined based on the plurality of GLMs. The conversion is performed based on the prediction.