Halftoning Module Error Diffusion Seed Generation
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Solution Overview
Problem
Conventional halftoning technologies face challenges such as 'start-up delay' near image edges, poor image quality due to error diffusion, and high computational requirements, particularly in simulating continuous tone images.
Innovation Solution
A processing method and device that generates an error diffusion value and detects edges in images, adding the pixel value with either the error diffusion value or an error diffusion seed based on detection results, using a halftoning module, detecting module, and adding module, which improves image quality and reduces computational costs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If error diffusion method is used for halftoning, then image quality is improved compared to dithering, but computational resources are significantly increased and worms appear in images
Solution Approach 1:
The patent applies preliminary action by pre-calculating and storing error diffusion values in a lookup table before actual halftoning processing. During runtime, the system simply retrieves pre-computed values based on pixel intensity levels, avoiding real-time error diffusion calculations. This resolves the contradiction by maintaining high image quality through error diffusion while dramatically reducing computational resource requirements during operation.
2Ease of operation
If conventional halftoning is used near image edges, then processing is straightforward, but start-up delay occurs because initial pixel gray values are lower than error diffusion threshold
Solution Approach 1:
The patent applies preliminary anti-action by detecting edge pixels beforehand and adding a specific offset value to their intensity before threshold comparison. This pre-compensation counteracts the start-up delay effect that would otherwise cause initial pixels near edges to fail the threshold test. The solution maintains processing simplicity while eliminating the time delay by preventing the threshold failure condition in advance.
3Measurement precision
If error diffusion value is added to current pixel, then halftoning accuracy is improved, but computational complexity increases
Solution Approach 1:
The patent applies copying by using pre-computed error diffusion values stored in a lookup table instead of calculating them dynamically. The system copies these pre-prepared values directly into the processing pipeline based on pixel intensity levels, maintaining halftoning accuracy through proper error diffusion while avoiding the computational complexity of real-time error diffusion calculations.
Data Source
AI summary
An image processing device comprises a halftoning module, a detecting module, an adding module and a generating module. The halftoning module generates an error diffusion value. The detecting module detects an edge at a current pixel of an image. The adding module adds the pixel value of the current pixel with one of the error diffusion value and an error diffusion seed according to the detecting result. The generating module generates the error diffusion seed. The error diffusion seed is generated by a formula based on the pixel value and location of the current pixel and a random number, or the error diffusion seed is generated by a formula based on the pixel value of the current pixel and a looked-up value, and the looked-up value is retrieved from a look-up table according to the location of the current pixel.