Printer Streak Characterization Using Wavelet Decomposition
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Solution Overview
Problem
Diagnosing the underlying causes of printer streaks is difficult due to the limitations of existing techniques, which primarily focus on streak detection and are unable to effectively characterize a broad range of defects in printers, making robust diagnosis and analysis of streaking defects impossible without fuller characterization.
Innovation Solution
The use of automated streak characterization techniques involving basis selection algorithms like matching pursuit, combined with wavelet decomposition, to identify and characterize dominant streaks in signal profiles, generating a printer streak characterization system that determines descriptor parameter sets for a streak template, including location, width, and intensity parameters, and updates the density profile iteratively.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional streak detection methods are used, then simple streak detection is achieved, but comprehensive streak characterization and diagnosis are impossible
Solution Approach 1:
The patent segments the streak analysis problem into multiple components: density profile generation from scanned images, wavelet decomposition into frequency components, and iterative basis selection to identify individual streak parameters. This segmentation allows comprehensive characterization while managing complexity through modular processing steps.
Solution Approach 2:
The patent transforms the streak detection problem from spatial domain analysis to frequency domain analysis using wavelet decomposition. By converting the density profile into frequency components, the system can identify streak patterns that are not apparent in the spatial domain, enabling more precise characterization without proportionally increasing system complexity.
2Measurement precision
If basis selection algorithms are used for streak characterization, then comprehensive streak parameters are identified, but computation complexity increases
Solution Approach 1:
The patent performs wavelet decomposition as a preliminary step before applying the basis selection algorithm. This preliminary action transforms the density profile into frequency components, which provides a structured representation that reduces the search space for the subsequent basis selection algorithm, thereby improving accuracy while managing computational complexity.
Solution Approach 2:
The patent implements an iterative basis selection process that identifies streak parameters progressively rather than all at once. By selecting a limited number of dominant streak components in each iteration and updating the density profile accordingly, the system achieves comprehensive characterization without requiring computationally exhaustive analysis of all possible streak parameters.
3Adaptability or versatility
If manual streak detection with predefined attributes is used, then specific streak types are detected, but robust diagnosis of broad range of defects is impossible
Solution Approach 1:
The patent develops a universal streak characterization system that can identify multiple streak types (horizontal, vertical, diagonal, curved) and various defect patterns through a single automated algorithm. The basis selection algorithm adapts to different streak patterns by selecting appropriate frequency components from the wavelet decomposition, providing versatile defect diagnosis without requiring separate detection methods for each streak type.
Data Source
AI summary
Methods and systems are presented for characterizing printer streaking using a basis selection algorithm to determine parameters set for a streak template that best approximate a density profile derived from a scanned printed test image, with wavelet decomposition used to expedite the searching for position and width parameters.