Evolved Transform Coefficient Optimization for Data Loss Reduction
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Solution Overview
Problem
Existing data processing technologies face challenges in minimizing data loss during quantization, compression, and transmission, particularly in wavelet-based lossy image compression schemes, where the number of coefficients in transforms is fixed, limiting the optimization of data loss.
Innovation Solution
The development of evolved transforms that can have a different number of coefficients than the original transforms, optimized using training data to minimize data loss during processes like quantization, compression, and transmission, allowing for adaptive coefficient selection to improve data reconstruction quality.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of energy
If the number of coefficients in wavelet transforms is fixed, then the transform structure is simple and easy to implement, but data loss during quantization and compression cannot be minimized effectively
Solution Approach 1:
The patent applies dynamics by making the number of coefficients in the transform adaptable rather than fixed. The system evolves the coefficient count based on training data to minimize data loss, allowing the transform to dynamically adjust its parameters for optimal performance in different compression scenarios
Solution Approach 2:
The patent changes the parameter of coefficient count from a fixed value to a variable that can be optimized. By evolving the number of coefficients based on training data, the system transforms a static parameter into an adaptive one that minimizes data loss during quantization and compression
2Measurement precision
If traditional fixed-coefficient transforms are used, then the implementation is straightforward, but mean squared error in reconstructed images cannot be reduced below certain limits
Solution Approach 1:
The patent applies preliminary action by evolving the transform coefficients and determining the optimal number of coefficients before actual compression operations. Training data is processed in advance to establish the optimized transform parameters, which then guide subsequent compression and reconstruction operations
Solution Approach 2:
The system uses feedback from training data to continuously optimize the transform parameters. By evaluating reconstruction quality on training data and adjusting the coefficient count accordingly, the system creates a feedback loop that improves reconstruction accuracy while managing complexity
3Loss of energy
If the transform coefficients are constrained to equal counts, then the mathematical structure is simplified, but optimization of data loss during compression is limited
Solution Approach 1:
The patent makes the coefficient count dynamic and adaptable rather than constrained to equal values. The system evolves different numbers of coefficients for different transform stages based on what minimizes compression loss, abandoning the rigid equal-count constraint for flexible, data-driven optimization
Data Source
AI summary
Methods and systems for processing data are disclosed. An example method can comprise receiving first data. The method can comprise applying a first transform to the first data. The first transform can be evolved from a second transform. The first transform can be based on first coefficients and the second transform can be based on second coefficients. The first transform can be evolved without constraining a count of the first coefficients to be equal to a count of the second coefficients. The method can comprise providing the transformed first data.