Two-Stage RGB Image Compression for Edge Preservation
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Solution Overview
Problem
Existing image compression methods, such as lossy JPEG compression, fail to preserve edges and white or gray pixels, leading to degraded image quality in printing applications, while lossless compression provides poor compression ratios.
Innovation Solution
A two-stage RGB compression process that truncates a 4×4 RGB pixel cell into four colors using a lossy fixed-length format, followed by lossless variable rate compression, preserving specific colors like white, black, or gray, and applying interpolation, clustering, or block truncation compression modes to optimize image quality and reduce storage demands.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of substance
If lossy compression process (e.g., JPEG) is used to achieve high compression ratio, then storage demand and bandwidth demand are reduced, but image quality deteriorates due to loss of edges and white or gray pixels
Solution Approach 1:
The image is divided into 4x4 pixel blocks, and each block is processed independently through clustering to identify dominant colors. This segmentation allows selective preservation of important color information while compressing less critical areas, resolving the contradiction between compression ratio and image quality.
Solution Approach 2:
The patent transforms the image representation by converting pixel values into cluster indices and color descriptors. This parameter transformation enables efficient compression while preserving visually important characteristics like edges and critical colors (white, gray), achieving both high compression ratio and maintained image quality.
2Manufacturing precision
If lossless compression process is used to preserve image quality, then edges and white or gray pixels are maintained, but compression ratio is poor and storage demand remains high
Solution Approach 1:
The patent changes the parameter representation from storing all pixel values to storing cluster indices and dominant color information. This parameter transformation achieves lossless preservation of important visual information while dramatically reducing storage requirements through efficient encoding of clustered data.
3Loss of time
If traditional compression methods are applied to reduce bandwidth demand for transfer, then transmission time is reduced, but image quality is degraded
Solution Approach 1:
The patent transforms image data into a compact representation using cluster indices and color descriptors, reducing the amount of data to be transmitted. This parameter transformation enables faster transmission (reduced bandwidth demand) while preserving critical image quality characteristics through intelligent data selection and efficient encoding.
Data Source
AI summary
A number of colors in a predefined region of an image to be compressed are determined. The predefined region has a plurality of pixels. In response to determining that the number of colors is greater than a first predefined threshold, a lossy compression process is selected dependent on a number of pixels in the predefined region having a predefined color. The selected lossy compression process is applied to the predefined region.