Linear Array Image Compression with Dynamic Pixel Window
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Solution Overview
Problem
High-performance image processing in printers is hindered by the need to quickly process and transport high pixel density images, which requires significant data computation and transportation resources, and existing compression techniques are either too complex or lose too much image quality.
Innovation Solution
A one-dimensional image compression technique that processes pixels in a row or column, using a window-based approach to replace extended run lengths of similar pixel values with a smaller data footprint, maintaining image quality by allowing dynamic assignment of pixel values and preventing super-long run lengths.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If high pixel density images are processed to maintain image quality, then image quality is preserved, but data computation and transportation resources increase significantly
Solution Approach 1:
The image processing is segmented into one-dimensional row or column processing rather than processing the entire two-dimensional image simultaneously. This divides the large data processing task into smaller, manageable segments that can be handled more efficiently with reduced computational resources.
Solution Approach 2:
The patent changes the processing parameter from two-dimensional image processing to one-dimensional processing. By processing pixels along a single row or column at a time, the computational complexity is reduced while maintaining the ability to preserve image quality through selective pixel value assignment.
2Productivity
If compression techniques are applied to reduce data size, then data transportation efficiency improves, but image quality is lost
Solution Approach 1:
The patent employs dynamic pixel value assignment where pixels within a window are assigned the same value only if they fall within a dynamically adjusted threshold range. This dynamic approach allows the compression technique to adapt to local image characteristics, preserving important image details while achieving compression, thus maintaining image quality.
Solution Approach 2:
The patent implements a feedback mechanism through threshold adjustment based on local pixel variation. The processing continues with adjusted thresholds to prevent super-long run lengths, using feedback from the local pixel statistics to control the compression process and maintain image quality.
3Manufacturing precision
If complex compression algorithms are used to maintain image quality, then image quality is preserved, but processing time and computational resources increase
Solution Approach 1:
By segmenting the processing into one-dimensional rows or columns and using a simple window-based approach with threshold comparison, the patent avoids complex algorithms while maintaining image quality. The segmented processing reduces computational overhead and processing time.
Solution Approach 2:
The patent simplifies the compression algorithm by changing from complex multi-dimensional processing to simple one-dimensional processing with dynamic thresholding. This parameter change reduces computational complexity and processing time while preserving image quality through adaptive threshold adjustment.
Data Source
AI summary
A method is described that includes defining a window of one or more consecutive pixel values in a linear array of pixel values of an image. The defining includes adding a pixel value in the linear array to the window so long as the pixel value does not deviate from the pixel values that exist within the window to a degree that causes a threshold value to be exceeded, and, wherein the threshold value decreases as the size of the window increases. The method also includes encoding the window of pixel values with a value determined from pixel values within the window and the number of pixel values within the window.