Adaptive Matrix Iteration for Stereo Image Processing
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Solution Overview
Problem
Current data processing methods for converting 2D image content to 3D stereo images face challenges due to limited computational capability in image processors, necessitating a reduction in computational complexity to enhance image processing efficiency.
Innovation Solution
A computer-implemented data processing method and device that selectively execute iterations with different computational complexities based on a target matrix's condition, using fixed-value and unfixed-value parameters to achieve iterative approximation, with the determining unit assessing the complexity and driving the appropriate iteration unit to obtain updated target matrices.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If iterative approximation with unfixed-value parameters is used to improve approximation accuracy, then manufacturing precision is improved, but device complexity increases
Solution Approach 1:
The patent applies dynamics by making the iteration parameters adaptive rather than fixed. The determining unit dynamically evaluates the target matrix characteristics and adjusts iteration parameters accordingly, allowing the system to transition between fixed-value and unfixed-value parameter modes based on actual computational needs, thus resolving the contradiction between accuracy and complexity
Solution Approach 2:
The patent changes parameters by introducing a determining unit that evaluates target matrix properties and selectively applies different iteration parameter strategies. When the target matrix exhibits certain characteristics, fixed-value parameters are used for efficiency; otherwise, unfixed-value parameters are applied for higher accuracy, thereby optimizing the balance between computational complexity and approximation quality
2Productivity
If fixed-value parameters are used to reduce computational complexity, then productivity is improved, but manufacturing precision deteriorates
Solution Approach 1:
The system dynamically selects between fixed-value and unfixed-value parameter modes based on real-time evaluation of target matrix characteristics. This dynamic adaptation allows the system to maximize processing efficiency when possible while maintaining approximation accuracy when needed, resolving the productivity-precision tradeoff
Solution Approach 2:
The patent implements parameter changes by introducing conditional logic that modifies iteration parameters based on target matrix evaluation. The determining unit assesses whether fixed-value parameters suffice for acceptable accuracy, and only invokes unfixed-value parameters when necessary, thus optimizing both productivity and precision
Data Source
AI summary
A computer implemented data processing method for recursively approximating a proper value for a target matrix includes the following steps of: determining whether the target matrix corresponds to a low complexity condition; if so, obtaining a first updated target matrix according to a first variance, relevant to a second iteration parameter, and a first iteration parameter, wherein the first and the second iteration parameters correspond to fixed values; if not, obtaining a second updated target matrix according to a second variance, relevant to a fourth iteration parameter, and a third iteration parameter, wherein the third and the fourth parameters are related to the target matrix.