Adaptive Intra Prediction Coding Unit Segmentation
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Solution Overview
Problem
Existing video codecs face inefficiencies in encoding and decoding high-resolution video content, as they are limited by fixed macroblock sizes and prediction modes, leading to increased compression information and decreased data compression efficiency.
Innovation Solution
The method involves splitting a maximum coding unit into smaller coding units, determining edge directions, grouping units with uniform edge directions, and performing intra prediction using a range of sizes to optimize coding unit sizes for efficient intra prediction mode determination.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If fixed macroblock sizes are used for encoding, then the encoding process is simple, but encoding efficiency for high-resolution video content decreases
Solution Approach 1:
The patent divides the fixed macroblock structure into variable-sized coding units that can be adaptively segmented based on image characteristics. The coding unit size is dynamically determined by analyzing edge components and gradient information, allowing the encoder to segment regions with different complexity levels appropriately, thereby improving encoding efficiency while maintaining processability through systematic segmentation rules.
Solution Approach 2:
The patent introduces dynamic coding unit size determination that adapts to the specific characteristics of each image region. Instead of using fixed macroblock sizes, the system dynamically adjusts coding unit dimensions based on edge density and gradient magnitude calculations, enabling the encoder to optimize performance for high-resolution video content while maintaining computational feasibility through defined dynamic adjustment mechanisms.
2Device complexity
If limited prediction modes are used, then the device complexity is low, but data compression efficiency decreases
Solution Approach 1:
The patent changes the parameter of prediction mode selection by determining the optimal intra prediction mode based on analyzed edge directions and gradient information. The system evaluates multiple prediction modes (including angular modes corresponding to different edge directions and planar modes) and selects the one that minimizes reconstruction error, thereby improving data compression efficiency without requiring complex prediction mode generation mechanisms.
Solution Approach 2:
The patent enables the encoder to automatically determine the best prediction mode for each coding unit by analyzing its own edge components and gradient characteristics. The self-service mechanism involves calculating edge directions, grouping adjacent coding units with uniform edge directions, and selecting prediction modes that naturally align with the detected edge structures, eliminating the need for complex external prediction mode generation while improving compression efficiency.
3Loss of time
If larger coding units are used, then processing time is reduced, but prediction accuracy for complex regions decreases
Solution Approach 1:
The patent applies local quality by determining coding unit sizes and prediction modes independently for different regions based on their specific characteristics. Regions with simple structures use larger coding units for faster processing, while regions with complex edge structures use smaller coding units for higher prediction accuracy. The edge component analysis and gradient magnitude calculations enable the system to identify local quality variations and apply appropriate coding strategies to each region, balancing processing time and prediction accuracy.
Solution Approach 2:
The patent segments the image into coding units of varying sizes based on edge component analysis. By dividing complex regions into smaller units with uniform edge directions and simple regions into larger units, the system achieves both prediction accuracy for complex areas and reduced processing time for simple areas. The segmentation is guided by edge direction uniformity and gradient characteristics, ensuring that each segment is optimally sized for its specific content.
Data Source
AI summary
Provided are a method and an apparatus for determining an intra prediction mode. The method includes: splitting a maximum coding unit into coding units of a first size; acquiring an edge direction of each of the coding units of the first size; grouping adjacent coding units of the first size based on uniformity of edge directions of the adjacent coding units of the first size; determining a size range of coding units to be applied to intra prediction on the intra prediction coding unit group; performing the intra prediction on the intra prediction coding unit group by using coding units having sizes included in the determined size range; and determining a coding unit for the intra prediction and an intra prediction mode of the coding unit by comparing costs according to the performed intra prediction on the coding units having the sizes included in the size range.