Halftone Error Diffusion via Block Boundary Prediction
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
In image processing systems, the error diffusion method for halftone processing requires significant memory to store longitudinal errors, which is inefficient and can lead to image distortion due to the need to process pixels row by row, especially when calculating output values for pixels without known diffused errors.
Innovation Solution
Divide image data into blocks with target pixels on block boundaries, allowing for error prediction and diffusion across both horizontal and vertical directions, enabling simultaneous processing of multiple blocks and reducing memory requirements.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If the error diffusion method processes pixels row by row using a line-based method, then the transversal error can be immediately processed after diffusion, but the longitudinal errors must be temporarily stored in memory, increasing memory requirements
Solution Approach 1:
The patent divides the image into multiple blocks and processes errors within each block independently. By segmenting the image data into blocks with target pixels on boundaries, the system can predict errors for target pixels without needing to store all longitudinal errors from previous rows, thus reducing memory requirements while maintaining error diffusion effectiveness
Solution Approach 2:
The patent applies preliminary action by predicting errors for target pixels before the standard error diffusion process. Target pixels on block boundaries have their errors predicted in advance, allowing the main error diffusion to proceed without temporarily storing all longitudinal errors, thereby reducing memory usage
2Device complexity
If the error diffusion method processes pixels row by row, then the calculation sequence is simple, but image distortion occurs due to large errors and the need to store longitudinal errors
Solution Approach 1:
By dividing the image into blocks and processing errors within each block independently, the patent reduces image distortion. The segmentation allows error diffusion to be performed with smaller data sets, improving precision while keeping the calculation sequence relatively simple
Solution Approach 2:
The patent performs preliminary error prediction for target pixels on block boundaries before the main error diffusion process. This preliminary action ensures that errors are accounted for in advance, reducing image distortion and improving quality without significantly complicating the overall calculation sequence
3Quantity of substance
If the error diffusion method uses a line-based approach processing one image row at a time, then memory usage is reduced compared to processing the entire image, but processing speed decreases due to sequential processing
Solution Approach 1:
The patent segments the image into multiple blocks that can be processed in parallel. By dividing the image data into blocks with target pixels on boundaries, the system can process multiple blocks simultaneously, improving processing speed while maintaining low memory usage through the segmented approach
Solution Approach 2:
The patent introduces a block-based dimensional organization that allows parallel processing. By organizing errors diffusion in blocks rather than strict sequential rows, the system can process multiple blocks at the same time, improving productivity while keeping memory requirements low through the block segmentation
Data Source
AI summary
An error diffusion method applied to halftone processing for image data. The image data comprise a plurality of pixels. The method comprising the steps of dividing the image data into a plurality of image blocks; selecting one of the pixels belonging to each of the image blocks as a target pixel, wherein the target pixel is located on the boundary of the corresponding image block; assigning a predicted error to the target pixel; and executing the error diffusion method on the rest of the pixels of the image blocks according to the predicted error of the target pixels of the image blocks. When the error diffusion is performed, target pixels are found in the image block and then predicted errors are assigned to the target pixels in order to calculate their output values. The target pixels are located at boundaries of the image blocks, and the predicted errors may be 0 or the transversal or longitudinal errors outputted from the pixels above the target pixels.