Image Processing Apparatus Error Diffusion Edge Detection
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Solution Overview
Problem
Conventional image processing apparatuses face challenges in reducing throughput and improving the reproducibility of high-definition parts like thin lines and characters, especially when dealing with half-tone images, as they often result in fuzziness at contour edges during error diffusion processing.
Innovation Solution
An image processing apparatus that blocks multiple-valued image data into pixel blocks, calculates and quantizes their average gradation values, detects edges, and adjusts the output order of pixels based on determined patterns to optimize error diffusion, thereby enhancing the reproducibility of high-definition parts.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If error diffusion processing is performed on high resolution image data, then image quality is improved, but throughput becomes excessively large due to heavy workload
Solution Approach 1:
The patent divides the image processing into two distinct paths: a first processing path for edge portions that prioritizes high definition reproduction, and a second processing path for non-edge portions that prioritizes processing speed. This segmentation allows different processing strategies to be applied to different regions, resolving the contradiction between image quality and throughput.
Solution Approach 2:
The patent applies different processing qualities to different regions of the image: edge portions receive intensive error diffusion processing with preserved pixel output orders to maintain sharpness, while non-edge portions use simplified processing with averaged gradation values to reduce computational load. This local differentiation optimizes both quality and efficiency.
2Productivity
If conventional error diffusion processing is used to reduce throughput, then processing speed improves, but edge portions become fuzzy and image quality deteriorates
Solution Approach 1:
The patent dynamically adjusts the processing method based on the characteristics of each pixel block. The edge detection unit identifies edge portions, and the system automatically switches between the first processing path (for edges) and second processing path (for non-edges), making the processing adaptive rather than static.
Solution Approach 2:
The patent changes key processing parameters based on detected edge characteristics: for edge portions, it preserves the original pixel output order and applies full error diffusion; for non-edge portions, it uses averaged gradation values and simplified processing. This parameter adaptation resolves the quality-speed tradeoff.
3Manufacturing precision
If pixel output order is preserved during error diffusion, then high definition reproduction of thin lines and characters is improved, but processing complexity increases
Solution Approach 1:
The patent extracts only the necessary information (edge detection results and pixel output orders) from the original image data and uses this extracted information to guide the processing. By taking out only what is needed for high definition reproduction and applying it selectively to edge portions, the system achieves high quality without processing the entire image with full complexity.
Solution Approach 2:
The patent performs preliminary edge detection and determines pixel output orders before the main error diffusion processing. This preliminary action allows the system to prepare the necessary information in advance, reducing the complexity of the subsequent processing steps while maintaining high definition reproduction capability.
4Productivity
If block-based processing is used to reduce workload, then throughput is reduced, but edge acutance may be compromised
Solution Approach 1:
The patent segments the image into pixel blocks and further divides each block into edge portions and non-edge portions. This multi-level segmentation allows the system to process blocks efficiently while maintaining edge acutance through the first processing path that preserves pixel output orders specifically for edge regions.
Solution Approach 2:
The patent applies different processing qualities to different regions within each pixel block: edge portions receive high-quality processing with preserved pixel output orders to maintain acutance, while non-edge portions use simplified processing with averaged values to reduce workload. This local differentiation resolves the contradiction between throughput and edge acutance.
Data Source
AI summary
A pixel output order pattern selecting section selects the first pixel output order pattern set by the pixel output order pattern setting section when the edge detecting section detects the edge portion in the pixel block, and selects any of the plurality of second pixel output order patterns stored in the pixel output order pattern storage section when the edge detecting section does not detect the edge portion in the pixel block. An image data generating section generates the image data of the pixel block in order that the dots may be output in conformity with the pixel output order pattern selected by the pixel output order pattern selecting section based on the gradation value obtained by the gradation converting section.