Adaptive Edge-Pattern Image Compression for Ultra-Low Bandwidth Links
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Solution Overview
Problem
Current image compression technologies are inefficient for ultra-low bandwidth applications, failing to maximize perceptual/subjective quality and exhibiting high computational complexity, making them unsuitable for low-bandwidth image transmission scenarios.
Innovation Solution
Adaptive Edge-Pattern-based Image Compression (AEPIC) method that encodes structural discontinuities using geometric and statistical modeling, prioritizing and transmitting only the most salient edge features to achieve scalable bit-streams with minimized bit cost and maximized subjective quality.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If data-driven compression methods are used to minimize pixel-wise distortion, then objective image quality is improved, but perceptual/subjective quality and data reduction ratio deteriorate in low bit-rate scenarios
Solution Approach 1:
The patent extracts and encodes only the most salient structural features (edges, contours, boundaries) from the image, rather than encoding all pixel data. This selective extraction of visually significant information maintains perceptual quality while achieving high data reduction ratios in low bit-rate scenarios
Solution Approach 2:
The patent applies different encoding strategies to different regions of the image based on their visual importance. Structurally significant regions (edges, boundaries) are encoded with higher precision while less important regions use coarser representation, optimizing both perceptual quality and compression efficiency
2Loss of information
If feature-based image compression techniques are used to improve perceptual quality, then subjective quality is improved, but computational complexity increases making them unsuitable for ultra-low bandwidth applications
Solution Approach 1:
The patent segments the image processing task into distinct stages: structural feature detection, feature prioritization, selective encoding, and progressive transmission. This segmentation allows each stage to be optimized independently, reducing overall computational complexity while maintaining perceptual quality
Solution Approach 2:
The patent implements partial encoding by transmitting only the most salient structural features rather than complete image data. This partial action approach achieves acceptable perceptual quality at ultra-low bit rates while dramatically reducing computational complexity and transmission requirements
3Loss of information
If classic transform-based compression techniques are used, then data reduction is achieved, but they are unsuitable for ultra-low bandwidth applications with runtime constraints
Solution Approach 1:
The patent performs preliminary structural feature detection and prioritization before compression and transmission. By pre-identifying and ranking the most salient features, the system enables rapid decoding and progressive image reconstruction, meeting runtime constraints in ultra-low bandwidth applications
Data Source
AI summary
An encoding-decoding method and system is provided where the encoding process or system are not a mirror image of the decoding process or system.