Video Coding Mode Selection Based on Complexity
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Solution Overview
Problem
Existing video encoding technologies face challenges in balancing resource usage and coding quality, particularly in selecting optimal coding modes and strategies to efficiently manage memory and bandwidth in devices that handle various video content.
Innovation Solution
The proposed solution involves an architecture that selects coding modes and strategies based on complexity levels, using cost metrics like rate distortion optimization, and adjusts bit depth and calculation domains to optimize resource usage and coding efficiency, employing block coding structures and multiple coding strategies to support high-efficiency video coding.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If complex coding modes and strategies are used to improve coding quality, then coding quality is improved, but device complexity and resource usage increase
Solution Approach 1:
The patent implements dynamic coding strategy selection that adapts to different video content characteristics, resolution levels, and device capabilities. The system dynamically adjusts between complex and simple coding modes based on real-time conditions, allowing high coding quality when needed while reducing complexity for appropriate scenarios.
Solution Approach 2:
The system changes coding parameters such as block size, transform type, and prediction mode based on content analysis. By adjusting these parameters dynamically, the system achieves high coding quality for complex content while using simpler parameters for easier content, thereby managing device complexity effectively.
2Manufacturing precision
If complex coding modes are used to improve coding quality, then coding quality is improved, but memory and bandwidth usage increase
Solution Approach 1:
The patent employs dynamic selection of coding modes that adapt to content characteristics and resource constraints. When memory and bandwidth are limited, the system automatically selects more efficient coding strategies that maintain quality while reducing resource consumption.
Solution Approach 2:
The system adjusts coding parameters such as quantization level, block size, and transform type to optimize the trade-off between coding quality and resource usage. By changing these parameters based on content analysis, the system achieves high quality output while minimizing memory and bandwidth requirements.
3Device complexity
If simple coding modes are used to reduce device complexity, then device complexity is reduced, but coding quality deteriorates
Solution Approach 1:
The patent implements a dynamic coding system that automatically selects between simple and complex modes based on content characteristics. For simple content, basic coding modes are used to reduce complexity, while for complex content, advanced modes are activated to maintain quality.
Solution Approach 2:
The system adjusts coding parameters dynamically based on content analysis. When content is simple, parameters are set for efficient processing with lower quality requirements. When content is complex, parameters are adjusted to enable higher quality coding, thus optimizing the balance between device complexity and coding quality.
4Productivity
If simple coding modes are used to reduce resource usage, then resource usage is reduced, but coding quality deteriorates
Solution Approach 1:
The patent employs dynamic coding strategy selection that adapts to both content characteristics and resource constraints. The system automatically adjusts between resource-efficient modes and quality-oriented modes based on real-time conditions, ensuring optimal performance under varying resource availability.
Solution Approach 2:
The system changes coding parameters such as block size, transform type, and quantization level based on content analysis and resource availability. This allows the system to achieve high resource efficiency for simple content while maintaining coding quality for complex content when resources permit.
Data Source
AI summary
A system may receive an input stream for a coding operation. The system may determine available coding modes for the coding operation. The system may include coding selection logic that may determine a coding mode in response to the based on the available selection of coding modes. The coding selection logic may use the selected coding mode to determine a coding strategy. The selection logic may send an indication of the selected coding mode and coding strategy to coding logic to support execution of the coding operation, which may use the selected coding mode and coding strategy.