Directional Edge Image Compression Using Neighbor Pixel Prediction
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Solution Overview
Problem
Existing image compression techniques, such as H.264, JPEG-LS, and JPEG standards, are inefficient in compressing and restoring edge areas of images due to the lack of similarity between pixel values, leading to deteriorated compression efficiency and picture quality.
Innovation Solution
A method involving predicting pixel values at directional edges within image blocks using neighbor pixel values, selecting an edge mode with minimal difference between predicted and actual values, and outputting edge mode data for compression, effectively addressing the inefficiencies in edge area compression.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If existing image compression techniques (H.264, JPEG-LS, JPEG) are applied to edge areas, then compression is performed on the entire image, but compression efficiency and picture quality deteriorate significantly because similarities between pixel values cannot be utilized in edge areas
Solution Approach 1:
The image is divided into multiple blocks, and each block is further segmented into edge areas and non-edge areas. Different compression techniques are applied to each segment: edge areas use prediction based on neighboring pixel values, while non-edge areas use traditional compression methods that exploit pixel similarity. This segmentation allows optimized handling of different image regions with distinct characteristics.
Solution Approach 2:
The patent applies different compression strategies to different regions within the same image block. Edge areas, which have high frequency content and low pixel similarity, receive specialized treatment using directional prediction filters. Non-edge areas with high pixel similarity continue to use traditional compression. This local quality approach ensures that each region is compressed with the most appropriate method for its characteristics.
2Device complexity
If existing image compression techniques are applied to edge areas, then the compression process is simplified and uniform across the image, but the ability to preserve picture quality in edge areas is lost
Solution Approach 1:
The compression system dynamically adapts its approach based on local image characteristics. For each block, the patent determines whether it contains edge areas and selects appropriate prediction filters accordingly. This dynamic adaptation allows the system to maintain relatively simple processing while achieving better results, as the complexity is activated only where needed (in edge areas) rather than being uniformly applied throughout the entire image.
3Quantity of substance
If pixel values at directional edges are compressed using traditional methods, then compression is applied uniformly, but the lack of similarity between pixel values in edge areas prevents effective compression
Solution Approach 1:
The patent introduces directional prediction filters as intermediary elements between the original pixel values and the compressed representation. These filters use neighboring pixel values to predict edge pixel values, creating an intermediate representation that captures the essential information while reducing redundancy. This intermediary approach enables effective compression of edge areas by transforming the data into a form that is more amenable to compression while preserving picture quality.
Data Source
AI summary
Provided are an apparatus and method for effectively compressing and restoring edge areas in an image. Therefore, by predicting values of pixels positioned at each directional edge, among pixels constructing a 2×2 block in a current image, using values of neighbor pixels of the pixels positioned at each directional edge, selecting an edge mode corresponding to a directional edge in which the difference between the predicted values and actual values of the pixels is a minimum, and outputting edge mode data indicating the edge mode instead of the predicted values corresponding to the edge mode, it is possible to effectively compress edge areas in which little similarity exists between pixel values, and accordingly improve a compression rate of edge areas.