Context-Based Prediction Weighting for Lossless Image Compression
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Solution Overview
Problem
Existing methods for lossless image compression face challenges in determining efficient prediction weights for color values at different image positions, leading to suboptimal compression efficiency due to reliance on static weights or complex dynamic ranking methods, which are computationally intensive and not suitable for all applications.
Innovation Solution
A method that determines prediction weights by collecting and combining indexes of best predictions from adjacent image positions, using statistical information to calculate the number of times each prediction was the best in a specific context, allowing for context-dependent prediction statistics and efficient weight calculation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If static weights are used for prediction, then device complexity is reduced, but prediction efficiency deteriorates
Solution Approach 1:
The patent implements dynamic prediction weights that adapt to local image characteristics. Instead of using fixed static weights, the system calculates context-dependent weights based on the specific prediction context at each image position, allowing the weights to dynamically adjust to local variations in image data while maintaining computational efficiency through context-based categorization.
Solution Approach 2:
The patent applies different prediction weights to different regions of the image based on local characteristics. By analyzing the prediction context (such as gradients, edges, or texture patterns) at each image position and assigning weights specifically tailored to that local region, the system achieves high prediction efficiency without requiring globally complex weight determination mechanisms.
2Measurement precision
If complex dynamic ranking methods are used for prediction weighting, then prediction efficiency is improved, but device complexity increases
Solution Approach 1:
The patent segments the prediction process into distinct context categories. Instead of using a single complex ranking method for all image positions, the system divides the image space into different prediction contexts (such as smooth regions, edge regions, texture regions) and applies simplified weight determination rules specific to each segment, reducing overall complexity while maintaining efficiency.
Solution Approach 2:
The patent changes the parameters used for weight determination based on the prediction context. Rather than always applying complex dynamic ranking, the system selects appropriate weight calculation parameters (such as gradient magnitude, directional information, or statistical properties) that match the local image characteristics, achieving high efficiency with computationally efficient parameter-based weight assignment.
3Loss of information
If a priori prediction ranking is used, then code size is reduced, but measurement precision of prediction efficiency is limited
Solution Approach 1:
The patent performs preliminary context analysis at each image position before entropy encoding. By determining the prediction context and selecting appropriate weights in advance (before the actual color value is known), the system can assign optimal prediction weights that lead to better prediction accuracy and smaller entropy codes, achieving a priori optimization without sacrificing prediction efficiency.
Data Source
AI summary
A method for prediction weights determination for use in compression (particularly image compression) and forecasting applications. Prediction weights determining significance of different predictions when calculating generalized prediction are calculated using specific context-based procedure. Information about already known best predictions in close neighborhood of the position being processed is used to form prediction context. Number of times each particular prediction was the best prediction in past in current prediction context is treated as a prediction weight.