Intra-Coding Block Partitioning for Low-Overhead Prediction
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Solution Overview
Problem
Existing block-based codecs face challenges in achieving high coding efficiency due to the trade-off between signaling overhead and prediction accuracy, particularly in intra-prediction, where larger blocks result in less accurate spatial prediction and increased signaling overhead.
Innovation Solution
The proposed solution involves partitioning a block into multiple partitions along a certain dimension, with each partition being one sample wide, and sequentially reconstructing or encoding these partitions using neighboring samples for prediction, followed by correcting the predictor with a prediction residual, thereby reducing signaling overhead and improving prediction accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If larger blocks are used for intra prediction, then signaling overhead is reduced, but prediction accuracy deteriorates due to increased mean sample distance
Solution Approach 1:
The patent divides a large block into multiple partitions along a predetermined dimension, with each partition being one sample wide. This segmentation allows the large block to be processed in smaller units, maintaining low signaling overhead while improving prediction accuracy by reducing the mean sample distance within each partition.
Solution Approach 2:
The patent applies different prediction approaches to different regions by processing partitions sequentially. Each partition uses local neighboring samples for prediction, ensuring that the prediction is adapted to local characteristics while maintaining overall efficiency.
2Measurement precision
If more partitions are created, then prediction accuracy improves due to reduced mean sample distance, but signaling overhead increases
Solution Approach 1:
The patent segments the block into partitions of one sample width along a predetermined dimension. This specific segmentation strategy maximizes prediction accuracy by minimizing mean sample distance while controlling signaling overhead through the regular, predictable partition structure.
Solution Approach 2:
The patent uses one-sample-wide partitions, which is an extreme form of segmentation that maximizes prediction accuracy. The regular pattern of this excessive segmentation allows for efficient coding without proportionally increasing signaling overhead.
3Measurement precision
If sequential partition reconstruction is used, then prediction accuracy improves by using neighboring samples, but processing complexity increases
Solution Approach 1:
The patent performs prediction for each partition sequentially in a predetermined order, using already reconstructed neighboring samples. This preliminary reconstruction of neighboring partitions provides accurate reference samples for subsequent partitions, improving overall prediction accuracy.
Solution Approach 2:
Each partition uses its own reconstructed neighboring samples as reference for prediction, making the prediction process self-contained and adaptive to local characteristics without requiring external intervention.
Data Source
AI summary
Block-based coding of a picture is rendered more effective by providing an intra-prediction coding concept according to which a certain block of the picture is intra-prediction coded using a certain intra-coding mode by partitioning the predetermined block into partitions along a certain dimension with the number of partitions being greater than two and/or the partitions being one sample wide along the certain dimension with the partitions being, for reconstruction purposes, sequentially subject to spatial prediction using the intra-prediction coding mode signaled for the certain block followed by correcting the thus obtained predictor using a prediction residual so that for preceding partitions a reconstruction of the samples is available to the decoder at the time of processing the next, then current, partition. Signaling overhead with respect to the partitioning may be left off or may be kept low.