Multitree Block Subdivision With Parameter Inheritance in Image Coding
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Solution Overview
Problem
Current image and video coding standards have limited flexibility in subdividing pictures into blocks, leading to inefficient coding due to a high side information rate for prediction parameters, especially when dealing with arbitrarily shaped objects, which increases the bit rate and coding complexity.
Innovation Solution
The use of multi-tree subdivision allows for the spatial division of information samples into leaf regions of varying sizes, enabling more granular setting of coding parameters by sharing parameters among intermediate regions, reducing the need for explicit transmission of parameters for each leaf region.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If multi-tree subdivision is used to subdivide pictures into blocks of various sizes, then adaptability to image content is improved, but device complexity increases due to multiple subdivision structures
Solution Approach 1:
The patent applies nested subdivision structures where a first subdivision structure (e.g., quadtree) is embedded within a second subdivision structure (e.g., binary tree). The first subdivision divides the picture into initial blocks, and the second subdivision further divides selected blocks from the first structure. This nesting allows flexible block size adaptation while organizing complexity in hierarchical layers, managing the trade-off between adaptability and device complexity.
2Loss of information
If inheritance is used to copy syntax elements from inheritance regions, then side information rate is reduced, but manufacturing precision deteriorates due to parameter copying instead of explicit coding
Solution Approach 1:
The patent implements inheritance by copying syntax elements (coding parameters) from an inheritance region to a current region. Instead of explicitly coding all parameters for every block, the decoder copies parameters from a parent or reference region and applies them to child regions. This reduces the bit rate for side information while maintaining sufficient coding precision through selective inheritance and override mechanisms where needed.
Data Source
AI summary
A better compromise between encoding complexity and achievable rate distortion ratio, and/or to achieve a better rate distortion ratio is achieved by using multitree sub-divisioning not only in order to subdivide a continuous area, namely the sample array, into leaf regions, but using the intermediate regions also to share coding parameters among the corresponding collocated leaf blocks. By this measure, coding procedures performed in tiles—leaf regions—locally, may be associated with coding parameters individually without having to, however, explicitly transmit the whole coding parameters for each leaf region separately. Rather, similarities may effectively exploited by using the multitree subdivision.