Resizing Image and Soft Mask Data for PDF Compositing
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Solution Overview
Problem
In printing applications, the dimensions of image and soft mask data streams often differ, necessitating systems and methods to process and composite print data streams where the dimensions of the image and soft mask are not the same.
Innovation Solution
A computer-implemented method that compares the sizes of image and soft mask data, scales them to a common size if necessary, and performs compositing operations using the scaled data, allowing for effective processing and compositing of print data streams with differing dimensions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If image and soft mask are processed with different dimensions, then processing flexibility is improved, but compositing operation correctness deteriorates
Solution Approach 1:
The system performs preliminary scaling of the soft mask to match the image dimensions before performing compositing operations. This preliminary action ensures that the mask and image are of compatible sizes, preventing incorrect compositing results while maintaining the flexibility to handle different dimensional inputs.
Solution Approach 2:
The scaling operation acts as an intermediary step between the input data (image and mask with potentially different dimensions) and the compositing operation. This intermediary process transforms the mask dimensions to match the image dimensions, enabling correct compositing while preserving processing flexibility.
2Reliability
If soft mask is scaled to match image dimensions, then compositing operation correctness is improved, but processing time increases
Solution Approach 1:
The system changes the dimensional parameters of the soft mask through scaling operations to match the image dimensions. This parameter change enables correct compositing operations while the scaling is performed efficiently using standard image processing algorithms that minimize time overhead.
3Manufacturing precision
If image and mask are scaled to common size, then compositing quality is improved, but data resolution may deteriorate
Solution Approach 1:
The scaling operation changes the dimensional parameters of the mask and image to common sizes, enabling high-quality compositing. The scaling is performed using interpolation methods that preserve as much original resolution detail as possible, balancing compositing quality with data resolution maintenance.
Data Source
AI summary
Methods disclosed permit compositing operations to be performed on images using an associated mask even in situations where the image and the mask differ in size. In some embodiments, image and mask data may be specified as a soft mask image in a page description language such as PDF. Scaling operations may be performed on the image, mask, or on both the image and mask when they differ in size. Compositing operations may be performed on the scaled image and/or mask after they have been scaled to the same size. Composting operations in situations where the original mask and image are of the same size are not affected by scaling operations.