Adaptive Coefficient Significance Trees for Lower-Bitrate Video Coding
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Solution Overview
Problem
Conventional tree-based encoding methods for video coding standards do not adequately account for the varying statistics and properties of video content, leading to inefficient encoding of coefficient significance in video compression.
Innovation Solution
Adaptive tree selection is employed to select from among multiple trees and sub-trees based on parameters, modifying nodes to better adapt to the variability of coefficient significance, and utilizing transforms and binary operators to enhance coding efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If conventional tree-based encoding is used to indicate coefficient significance, then the encoding process is efficient when divided into two steps (locations first, then level and sign), but the encoding becomes expensive because it does not properly account for varying statistics and properties across coefficients and content
Solution Approach 1:
The patent applies dynamics by making the tree structure adaptive rather than static. Multiple tree structures are defined with different node arrangements, and the encoder dynamically selects the most appropriate tree structure based on the statistical properties and characteristics of the current video block's coefficients. This allows the encoding process to adapt to varying content statistics, improving compression efficiency while maintaining accurate coefficient significance representation.
Solution Approach 2:
The patent changes the structural parameters of the tree by defining multiple tree structures with different configurations (e.g., different groupings of coefficient positions at various levels). By selecting among these predefined tree structures based on coefficient statistics, the system optimizes the encoding cost for indicating coefficient significance locations, thereby reducing bit rate while preserving encoding efficiency.
2Device complexity
If a single fixed tree structure is used for encoding coefficient significance, then the device complexity is low, but the adaptability to different video content statistics and properties is insufficient
Solution Approach 1:
The patent implements universality by designing multiple tree structures that can universally handle different types of video content and coefficient statistics. Each tree structure is configured to be effective for specific content characteristics, and the system selects the appropriate tree structure based on the input content, making the encoding system universally applicable to diverse video materials while maintaining manageable complexity through predefined structures.
Solution Approach 2:
The system achieves adaptability through dynamic selection among multiple predefined tree structures. Rather than using a single fixed structure, the encoder evaluates the statistical properties of the current block and dynamically chooses the most suitable tree structure from the available options, enabling the system to adapt to varying content characteristics without requiring complex adaptive algorithms.
Data Source
AI summary
Methods and apparatus are provided for encoding and decoding binary sets using adaptive tree selection. In one exemplary encoding method embodiment, picture data is encoded for a block in a picture; in which one of a plurality of trees structures is selected to code a binary set of data for indicating coefficient significance for the block. In another exemplary encoding method embodiment, picture data is encoded for a block in a picture, in which one or more trees are used to encode a binary set of data for indicating coefficient significance for the block, the one or more trees each having a plurality of nodes, at least one of the nodes of the one or more trees being modified responsive to at least one parameter.