Compensation Table Compression via Rate Distortion Optimization
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current compression methods for compensation tables in display technology face challenges in achieving optimal compression ratios and time complexity, leading to subpar data transmission efficiency due to varying encoding tool performances.
Innovation Solution
A compensation table compressing method that divides tables into encoding blocks, processes them using multiple prediction modes (simple inter-frame, linear model, and intra-frame predictions), and applies a rate distortion optimizing method to select the optimal prediction mode, transforming and quantifying prediction errors for improved compression.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If a common universal encoder is used to compress compensation tables, then encoding simplicity is maintained, but compression efficiency and time complexity performance deteriorate
Solution Approach 1:
The compensation table is divided into multiple encoding blocks, with each block processed independently using multiple prediction modes. This segmentation allows parallel processing and optimization of each block, improving overall compression efficiency while maintaining manageable complexity through modular organization
Solution Approach 2:
The invention changes the encoding parameters by introducing multiple prediction modes (simple inter-frame, linear model, and intra-frame prediction) and using rate distortion optimization to select the best mode for each block. This parameter variation enables adaptive compression that achieves better efficiency without requiring a complete redesign of the encoding system
2Manufacturing precision
If multiple prediction modes are processed for each encoding block, then compression quality is improved, but time complexity increases
Solution Approach 1:
Prediction values for multiple modes are pre-calculated for each encoding block before the final selection. This preliminary action allows the rate distortion optimization to quickly compare pre-computed values and select the best mode, reducing the time penalty of evaluating multiple prediction modes
Solution Approach 2:
The rate distortion optimization mechanism provides feedback by calculating the distortion and bit rate for each prediction mode, then selecting the mode that minimizes the rate distortion cost function J=D+λ×R. This feedback-driven selection ensures high compression quality while avoiding exhaustive search through all possible modes
3Productivity
If compression ratio is reduced to improve compression quality, then data transmission efficiency is improved, but encoding complexity increases
Solution Approach 1:
By dividing the compensation table into encoding blocks and applying different prediction modes to different blocks, the system achieves better compression ratios through localized optimization. The segmentation allows complex multi-mode processing to be applied only where needed, managing encoding complexity through distributed processing
Solution Approach 2:
The invention uses rate distortion optimization with the cost function J=D+λ×R to dynamically adjust encoding parameters. By varying the lagrange multiplier λ and selecting different prediction modes based on the optimization result, the system achieves improved compression quality while controlling encoding complexity through adaptive parameter selection
Data Source
AI summary
A compensation table compressing method is provided, processes each encoding block by prediction modes and select one prediction mode with the minimum rate distortion optimizing value as an optimized prediction mode by the rate distortion optimizing method such that each encoding block can correspond to an optimized prediction mode, which lowers the compression ratio of the compensation table and the time complexity of encoding and increases the quality of compression.