High-speed cell-based image compression for printing
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Solution Overview
Problem
Printing devices face challenges in efficiently compressing and decompressing electronic documents due to the need for both lossless and lossy compression techniques to handle various image types, which affects storage and transmission efficiency.
Innovation Solution
The method involves classifying pixels into P and Q class cells, creating intermediate pixel maps, and encoding them using lossless and lossy compression techniques, with P cells remaining unchanged and Q cells being downscaled and serialized, and vice versa, to optimize storage and transmission.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If lossless compression is applied to all image data, then image quality is preserved, but storage efficiency deteriorates
Solution Approach 1:
The patent applies different compression methods to different regions of the image based on their characteristics. P-class cells (containing important visual information) use lossless compression to preserve quality, while Q-class cells (containing less important information) use lossy compression to improve storage efficiency. This local differentiation resolves the contradiction by applying the appropriate compression level to each region rather than uniformly across the entire image.
2Quantity of substance
If lossy compression is applied to reduce storage size, then storage efficiency improves, but image quality deteriorates
Solution Approach 1:
The patent classifies image cells into P-class and Q-class based on visual importance, then applies lossless compression to P-class cells and lossy compression to Q-class cells. This ensures that image quality is preserved in critical regions while storage efficiency is improved in less critical regions, resolving the contradiction between storage efficiency and image quality.
3Quantity of substance
If complex compression algorithms are used to achieve high compression ratio, then storage efficiency improves, but processing time increases
Solution Approach 1:
The patent divides the image into multiple cells and classifies them into P-class and Q-class categories. This segmentation allows the system to apply appropriate compression algorithms to each class, using simpler algorithms for Q-class cells and more complex algorithms only for P-class cells, thereby reducing overall processing time while maintaining good compression efficiency.
4Device complexity
If uniform compression is applied to the entire image, then processing complexity is reduced, but compression efficiency deteriorates
Solution Approach 1:
The patent implements a classification system that analyzes each cell's visual characteristics and assigns it to P-class or Q-class. This local quality assessment enables the system to apply optimal compression methods to each region, significantly improving overall compression efficiency while managing processing complexity through automated classification.
Data Source
AI summary
An example embodiment may involve obtaining an input pixel map of a digital image containing an array of a×b pixel macro-cells; classifying each of the a×b pixel macro-cells as P class cells for substantially lossless compression or Q class cells for lossy compression; creating a first intermediate pixel map representing: the P class cells as is, and the Q class cells with all zero values; creating a second intermediate pixel map representing: the P class cells with all zero values, and the Q class cells as is; encoding the first intermediate pixel map into a first output stream by using substantially lossless compression; and encoding the second intermediate pixel map into a second output stream by: downsampling the P class cells and the Q class cells therein, and serializing representations the downsampled cells.