Adaptive MPM List Construction for Video Intra Prediction
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Solution Overview
Problem
Current video coding standards face challenges in achieving a balance between bandwidth requirements and video quality, particularly with the increasing complexity of intra mode coding schemes and the need for adaptive index lists based on block properties.
Innovation Solution
The proposed method involves constructing a Most Probable Mode (MPM) list for intra prediction by obtaining the intra prediction mode of a neighbor block and adding it to the MPM list if it meets a predetermined threshold of predicted samples, thereby simplifying the calculation procedure and improving coding efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If the intra mode coding scheme uses a constant index list (non-selected mode set), then the implementation is simpler, but the coding efficiency is reduced due to lack of adaptability to block properties
Solution Approach 1:
The patent applies dynamics by transitioning from a static, constant index list to a dynamic MPM list that adapts to each current block's properties. The MPM list is constructed adaptively based on neighboring block modes and current block characteristics (size, shape, position), allowing the system to optimize coding efficiency for each block while maintaining manageable complexity through standardized construction procedures.
2Adaptability or versatility
If the MPM list is constructed using intra prediction modes of all neighboring blocks without filtering, then more candidates are available, but the list becomes too long and complex to manage
Solution Approach 1:
The patent applies local quality by selectively including neighboring block modes in the MPM list based on their relevance to the current block. Not all neighboring blocks contribute equally - the patent filters candidates based on spatial relationships, block availability, and mode compatibility, giving different weights to different neighbors. This creates a localized, optimized candidate set rather than a universal exhaustive list.
Solution Approach 2:
The patent extracts only the most relevant intra prediction mode candidates from the set of all neighboring block modes. By filtering out redundant or less relevant candidates (such as modes from unavailable neighbors or modes that don't match current block characteristics), the system maintains a compact MPM list with typically 3-6 candidates, balancing candidate availability with list manageability.
3Measurement precision
If more intra prediction modes are used (e.g., 67 modes in BMS), then the prediction accuracy improves, but the computational complexity and bandwidth requirements increase
Solution Approach 1:
The patent applies partial action by using a selective subset of available intra prediction modes rather than all 67 modes. The MPM list contains only the most probable modes (typically 3-6 candidates) that are most likely to be optimal for the current block. This partial approach achieves high prediction accuracy for each block while significantly reducing computational complexity compared to evaluating all modes, as the encoder and decoder only need to handle the compact MPM list plus a remainder code.
Data Source
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AI summary
A method for constructing a Most Probable Mode (MPM) list is provided. The method comprising: obtaining an intra prediction mode of a neighbor block adjacent to a current block;if the intra prediction mode of the neighbor block is not comprised in the MPM list and the intra prediction mode of the neighbor block is a directional(for example, angular) intra prediction mode, determining a quantity of predicted samples (N) of the current block according to positions of a set of reference samples on a boundary of the current block, N is an integer; if the N is greater than or equal to a threshold (for example, a first threshold), adding the intra prediction mode of the neighbor block into the MPM list.