Cell-Based Image Compression with Edge Detection
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Solution Overview
Problem
Printing devices face high storage requirements due to large electronic documents, and existing compression techniques either lose image quality or are inefficient, especially when handling mixed content like text, graphics, and photorealistic images.
Innovation Solution
A cell-based compression technique that differentiates between edge and non-edge pixels, using various encoding methods like D1, D1D, P2, P2D, DCT 4:2:0, and intelligent downsampling to efficiently compress images while preserving sharp edges and lines, allowing for both lossless and lossy compression within the same document.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If existing compression techniques are used to reduce storage requirements, then storage space is reduced, but image quality is lost
Solution Approach 1:
The patent segments the image into multiple planes (luminance plane, color planes) and further divides each plane into macro-cells containing pixel cells. This segmentation allows different compression techniques to be applied to different regions based on their content characteristics, preserving quality in edge regions while achieving compression in smooth regions.
Solution Approach 2:
The patent applies different compression strategies to different regions of the image based on edge detection results. Edge-weighted encoding is applied to regions containing edges to preserve sharpness, while standard encoding is applied to non-edge regions to achieve higher compression ratios.
2Productivity
If compression is applied to reduce document size, then storage and transmission efficiency is improved, but image quality degradation occurs
Solution Approach 1:
The patent dynamically selects encoding methods based on local image characteristics. The edge detection results guide the selection between edge-weighted encoding and standard encoding for each macro-cell, allowing the compression system to adapt to local content requirements and maintain quality where needed while maximizing compression elsewhere.
Solution Approach 2:
The patent changes encoding parameters based on edge detection results. Edge-weighted encoding modifies the quantization parameters and weighting factors in the DCT domain to preserve edge information, while standard encoding uses conventional parameters for maximum compression in non-edge regions.
3Device complexity
If uniform compression is applied to all image regions, then processing is simplified, but edge sharpness is lost
Solution Approach 1:
The patent segments the image processing into distinct stages: edge detection, macro-cell division, and region-specific encoding. This segmentation allows the system to apply complex edge-preserving techniques only where necessary while using simpler encoding elsewhere, managing overall complexity while preserving edge sharpness.
Solution Approach 2:
The patent performs edge detection and macro-cell classification before the actual compression encoding. This preliminary action identifies regions requiring edge-preserving treatment, allowing the subsequent encoding stage to apply appropriate techniques efficiently without trial-and-error adjustments.
Data Source
AI summary
An example embodiment may involve obtaining (i) an a×b attribute macro-cell, and (ii) a×b pixel macro-cells for each of a luminance plane, a first color plane, and a second color plane of an input image. The a×b pixel macro-cells may each contain 4 non-overlapping m×n pixel cells. The example embodiment may also involve determining 4 attribute-plane output values that represent the 4 non-overlapping m×n attribute cells, 1 to 4 luminance-plane output values that represent the a×b pixel macro-cell of the luminance plane, a first color-plane output value to represent the a×b pixel macro-cell of the first color plane, and a second color-plane output value to represent the a×b pixel macro-cell of the second color plane. The example embodiment may further involve writing an interleaved representation of the output values to a computer-readable output medium.