Edge Classification in Compressed Image Processing
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Solution Overview
Problem
Existing image processing systems face challenges in efficiently classifying edges in compressed images, leading to increased costs and complexity due to the need for larger memory and processing circuits when expanding images, especially when judging edges after compression or during resolution conversion.
Innovation Solution
An image processing apparatus and method that includes an edge classification section which distinguishes and classifies edges in expanded images using identification data indicating half tone or high resolution regions, allowing for edge classification with a simpler configuration by utilizing quantized data from compressed images.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If edge judgment is performed after expansion of compressed image, then edge classification accuracy is improved, but line memory size and processing circuit complexity increase
Solution Approach 1:
The patent performs edge classification on the compressed image before expansion, rather than after. The edge classification section analyzes the compressed image data directly to identify edge patterns, and this classification information is then used during the expansion process to guide edge enhancement. This preliminary action avoids the need to hold and process large amounts of expanded image data in line memory, thereby reducing memory size and circuit complexity while maintaining edge classification accuracy.
2Quantity of substance
If resolution conversion to lower resolution is performed for saving, then storage cost is reduced, but edge judgment cost increases when converting back to original resolution
Solution Approach 1:
The patent performs edge classification on the compressed image at lower resolution before saving, rather than performing edge judgment after resolution conversion back to original resolution. The edge classification section identifies edge patterns in the compressed state, and this classification information is preserved and used during subsequent expansion and resolution conversion processes. This approach maintains storage cost benefits while avoiding the increased complexity of edge judgment at high resolution.
Data Source
AI summary
Disclosed is an image processing apparatus including: an edge classification section for distinguishing an edge pattern of an edge to be included in an expanded image of a compressed image and classifying the edge by using the distinguished edge pattern, wherein the edge classification section distinguishes the edge pattern by using identification data assigned to each of the pixels of the compressed image, the identification data indicating a half tone region or a high resolution region with respect to each of the pixels, and quantized data of each of the pixels.