Video Encoder Split Mode Decision Using Texture Prediction
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing video encoding methods face complexity in reducing encoding time while maintaining compression efficiency, as they often collide when used together, leading to increased encoding time without significant compression performance improvements.
Innovation Solution
A method for video encoding that determines a split mode for a block using a texture-based split prediction set, adapting it according to non-redundancy constraints or heuristic-based pruning, ensuring that allowed split modes are evaluated in the rate-distortion optimization process, thereby reducing encoding complexity without compromising compression efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If multiple encoding optimization methods are used together to reduce encoding complexity, then encoding time is reduced, but the methods collide and cause encoding time to increase without significant compression performance improvement
Solution Approach 1:
The patent introduces a texture-based split prediction set as an intermediary mechanism that mediates between multiple encoding optimization methods. This prediction set acts as a filter that pre-selects candidate split modes based on texture analysis, allowing subsequent optimization methods to work on a reduced set of candidates rather than all possible split modes, thereby preventing method collisions while maintaining encoding speedup
Solution Approach 2:
The patent performs preliminary texture-based split mode prediction before the actual rate-distortion optimization process. By analyzing texture characteristics in advance and generating a prediction set of likely split modes, the system prepares a reduced candidate set that eliminates unnecessary evaluation of unlikely modes, preventing conflicts between optimization methods and reducing overall encoding complexity
2Device complexity
If the number of split configurations to be evaluated is reduced to decrease encoding complexity, then encoding time is reduced, but compression efficiency may be compromised
Solution Approach 1:
The patent applies local quality by adapting the split mode evaluation strategy to local texture characteristics. Different regions of the image block are analyzed for their texture properties, and the split prediction set is customized for each region's characteristics. This ensures that compression efficiency is maintained in regions requiring detailed analysis while reducing complexity in regions where simple split modes suffice
Solution Approach 2:
The patent changes the parameter set by dynamically adjusting which split modes are evaluated based on texture analysis results. Instead of using a fixed set of split configurations, the system modifies the candidate split mode set according to local texture parameters, ensuring adequate compression efficiency where needed while reducing unnecessary evaluations elsewhere
Data Source
AI summary
A method for encoding a block is disclosed. To this aim, a split mode is determined based on a rate-distortion optimization using a texture-based split prediction set obtained for the block. As an example, the split mode is determined by adapting the texture-based split prediction set according to at least one of a binary or triple split mode non-redundancy constraint or a heuristic-based split mode set pruning. The block is finally encoded using the determined split mode.