Prediction Filter Regularization for IntraTMP and IBC Blocks
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Solution Overview
Problem
Existing video coding systems face challenges in efficiently compressing digital video signals, particularly in adapting prediction filters based on prediction modes and block sizes, leading to suboptimal encoding and decoding performance.
Innovation Solution
Implementing regularization in prediction filters by generating matrices based on prediction modes and block sizes, and adaptively employing regularization based on template cost comparisons to optimize filtering parameters.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If prediction filters are adapted based on prediction modes and block sizes, then encoding and decoding performance is improved, but device complexity increases
Solution Approach 1:
The patent applies different filtering operations and regularization matrices to different prediction modes (IntraTMP vs. IBC) and different block size ranges. This local differentiation optimizes performance for each specific case without requiring a completely complex adaptive system for all scenarios, resolving the contradiction by making the system complexity proportional to the actual performance needs of each prediction case.
2Measurement precision
If regularization matrices are generated based on prediction modes and block sizes, then filtering precision is improved, but computational resources increase
Solution Approach 1:
The patent changes the regularization parameter based on prediction mode and block size characteristics. By selecting from predefined regularization matrices or adjusting the regularization strength parameter according to these conditions, the system achieves higher filtering precision when needed while avoiding unnecessary computational overhead in cases where strong regularization is not required, thus resolving the contradiction between precision and computational resources.
3Reliability
If adaptive regularization is employed based on template cost comparisons, then prediction accuracy is improved, but processing time increases
Solution Approach 1:
The patent implements adaptive regularization by comparing template costs, but applies the full regularization operation only when the comparison indicates it is beneficial. In cases where the template cost comparison shows that regularization would not provide significant improvement, the system skips or reduces the regularization step. This partial application of regularization maintains prediction accuracy where needed while reducing processing time in cases where it would be wasteful, resolving the contradiction between accuracy and processing time.
Data Source
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AI summary
Systems, methods, and instrumentalities are disclosed for performing regularization in prediction filters. A device, which may be an encoder device and/or a decoder device, may determine for a picture block that a prediction mode is one of IntraTMP or IBC. The device may determine, using the prediction mode, a prediction block associated with the picture block. The device may generate, based on a current template and a reference template, a first matrix associated with pixel values in the reference template and/or a second matrix associated with vectors associated with the current template. The device may generate a regularization matrix. The device may be configured to determine, using the at least one matrix associated with the filtering function and the regularization matrix, filtering parameters. The device may filter the prediction block using the determined filtering parameters and may decode and/or encode the picture block using the filtered prediction block.