Intra Subpartition Coding With Quad-Splits and Secondary Transform
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Solution Overview
Problem
Current video coding technologies face limitations in using Low Frequency Non-Separable Transform (LFNST) for intra subpartitions, leading to inefficiencies in coding efficiency and bit rate, and restrict intra subpartitions to only horizontal or vertical splits without considering local variations in prediction modes.
Innovation Solution
Implement a secondary transform at the transform unit level for intra subpartitions, allowing quad-splits and partition-specific intra prediction modes, along with a predetermined rule to determine individual intra-prediction for each partition, enhancing coding efficiency and reducing bit rate.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If LFNST is used for intra subpartitions, then coding efficiency is improved, but it is restricted to only horizontal or vertical splits without considering local variations in prediction modes
Solution Approach 1:
The block is divided into multiple subpartitions (e.g., 2x2 subpartitions) allowing independent prediction mode selection for each subpartition. This segmentation enables the system to overcome the limitation of applying a single prediction mode across the entire block, thereby improving adaptability while maintaining coding efficiency through targeted transform application.
Solution Approach 2:
Different prediction modes can be applied to different subpartitions based on local characteristics. Each subpartition can have its own intra-prediction mode independently selected from available modes, allowing the system to adapt to local variations in prediction requirements while maintaining overall coding efficiency.
2Device complexity
If intra subpartitions are restricted to horizontal or vertical splits, then device complexity is reduced, but adaptability to local variations in prediction modes is limited
Solution Approach 1:
The block is divided into multiple subpartitions (e.g., 2x2 subpartitions) allowing independent prediction mode selection for each subpartition. This segmentation enables the system to overcome the limitation of applying a single prediction mode across the entire block, thereby improving adaptability while maintaining coding efficiency through targeted transform application.
Solution Approach 2:
Different prediction modes can be applied to different subpartitions based on local characteristics. Each subpartition can have its own intra-prediction mode independently selected from available modes, allowing the system to adapt to local variations in prediction requirements while maintaining overall coding efficiency.
3Productivity
If a secondary transform is applied to intra subpartitions, then coding efficiency is improved by condensing non-zero coefficients, but device complexity increases
Solution Approach 1:
The secondary transform (LFNST) is applied selectively only to certain subpartitions where it provides benefit, rather than universally to all blocks. The transform is applied based on conditions such as the presence of non-zero coefficients or specific prediction modes, allowing the system to gain coding efficiency improvements while limiting the increase in device complexity to only where necessary.
Data Source
AI summary
Decoder for decoding a picture, wherein blocks into which the picture is subdivided are assigned to a set of prediction types, which include intra prediction and inter prediction. For each of predetermined intra-predicted blocks, the decoder is configured to derive, from the data stream, an information on a partitioning of the respective predetermined intra-predicted block into partitions. The decoder is configured to derive, from the data stream, for each partition of the respective predetermined intra-predicted block, a partition-specific prediction residual signal related to a spatial domain prediction residual signal of the respective partition of the respective predetermined intra-predicted block via a predetermined transform. The predetermined transform can be identified out of a set of transforms including a first transform and a second transform, which equals a concatenation of a primary transform and a secondary transform applied onto a subset of coefficients of the primary transform.


