Threshold Value Matrix Sub-Matrix Replacement for Inkjet Nozzle Failures
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Solution Overview
Problem
Existing image processing methods for inkjet printers face challenges in maintaining high image quality when dealing with ink ejection failures, particularly when multiple nozzles malfunction, as they require large storage volumes for threshold value matrices, which is impractical and incompatible with blue noise masks.
Innovation Solution
The method involves creating a basic threshold value matrix and corresponding sub-matrices to replace specific regions around malfunctioning nozzles, allowing for selective substitution to minimize visible defects without significantly increasing storage volume, using a prescribed pixel width for the sub-matrices.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If a large-size threshold value matrix (blue noise mask) is used for high-quality image formation, then image quality is improved, but storage volume becomes excessively large when multiple dithering matrices are prepared for ejection failure positions
Solution Approach 1:
The patent divides the large threshold value matrix into multiple sub-matrices, each corresponding to a specific ejection failure position. Instead of storing complete alternative matrices, only the affected sub-matrix regions are stored and replaced as needed, dramatically reducing storage requirements while maintaining image quality.
Solution Approach 2:
The patent applies local replacement of threshold values only in the sub-matrix region affected by ejection failure, rather than replacing the entire matrix. This localized approach maintains high image quality in unaffected regions while correcting defects only where needed, optimizing both quality and storage efficiency.
2Manufacturing precision
If multiple complete dithering matrices are prepared for each ejection failure position, then image quality is maintained, but device complexity increases due to matrix management
Solution Approach 1:
The patent segments the threshold value matrix into sub-matrices and stores only the necessary correction data for each ejection failure position. This segmentation simplifies matrix management by reducing the number of complete matrices that need to be stored and managed, while still providing comprehensive coverage for all possible failure positions.
Solution Approach 2:
The patent pre-calculates and stores only the sub-matrix corrections needed for each ejection failure position rather than preparing complete alternative matrices. This preliminary preparation reduces the complexity of real-time matrix selection and management during actual printing operations.
3Quantity of substance
If the threshold value matrix size is reduced to decrease storage volume, then storage requirements are reduced, but image quality deteriorates
Solution Approach 1:
The patent enables the use of large-size blue noise masks for high-quality image formation by segmenting them into sub-matrices. This segmentation allows the system to store and manage only the necessary correction portions rather than complete large matrices, making large-matrix-based halftoning practically feasible with acceptable storage requirements.
Solution Approach 2:
The patent changes the storage parameter from complete matrices to sub-matrix segments, allowing the use of large threshold value matrices (256×256 or 512×512) for high-quality image formation while keeping storage volumes manageable through selective storage of only the correction portions.
Data Source
AI summary
The image processing method comprises: a recording failure position determination step of determining a recording failure position on an image corresponding to a malfunctioning recording element; a basic threshold value matrix storage step of storing a basic threshold value matrix set with threshold values used for halftoning of converting multiple-value input image data into dot data of a number of tonal graduations smaller than that of the multiple-value input image data by quantizing the multiple-value input image data; a sub-matrix storage step of storing a plurality of sub-matrices in association with recording failure positions in the basic threshold value matrix, each of the sub-matrices being set with threshold values which are substituted for the threshold values in a partial region of the basic threshold value matrix, the partial region including a pixel position corresponding to the recording failure position and having a width of a prescribed number of pixels; a replacement region determination step of determining a corresponding region with the width of the prescribed number of pixels including the recording failure position in the basic threshold value matrix, according to the pixel position in the input image data and the recording failure position determined in the recording failure position determination step; a sub-matrix selection step of selecting one of the sub-matrices stored in the sub-matrix storage step to use for substituting for the corresponding region determined in the replacement region determination step; a threshold value replacement step of creating a reformed threshold value matrix by replacing the threshold values of the corresponding region including the recording failure position in the basic threshold value matrix, with the one of the sub-matrices selected in the sub-matrix selection step; and a quantization processing step of quantizing the input image data by selectively using the basic threshold value matrix and the reformed threshold value matrix.