YHB Color Space Compression for Image Data
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Solution Overview
Problem
Current lossy compression methods, such as JPEG, fail to efficiently compress digital images without losing perception, leading to suboptimal compression rates and increased data storage and transmission requirements.
Innovation Solution
The YHB color space is introduced, which separates the blue component and uses additive red and green information, along with the difference between red and green for subsampling, allowing for higher compression ratios without affecting image perception.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If conventional lossy compression (JPEG/YUV) is used to reduce image size, then data storage and transmission requirements are reduced, but compression efficiency is suboptimal and perception quality deteriorates
Solution Approach 1:
The invention segments the image data into three separate channels (Y, H, B) based on human visual system characteristics, allowing independent processing and subsampling of each channel. This segmentation enables more efficient compression by treating different color components differently according to their perceptual importance.
Solution Approach 2:
The invention changes the color space parameters from traditional RGB or YUV to YHB color space, fundamentally altering how color information is represented. This parameter change enables higher compression ratios by exploiting the specific characteristics of human color perception, particularly the lower sensitivity to blue channel variations.
2Quantity of substance
If higher compression ratios are achieved by discarding color information, then data size is reduced, but image perception quality is lost
Solution Approach 1:
The invention applies different quality levels to different color channels based on human visual sensitivity. The Y (luminance) channel maintains full resolution and quality, while the H (hue) and B (blue) channels are heavily subsampled. This local quality differentiation ensures that perception-critical information is preserved while non-critical information is compressed.
Solution Approach 2:
The invention discards redundant color information that is less critical to human perception (particularly blue channel variations) while retaining essential luminance information. During decompression, the discarded information is recovered through interpolation and reconstruction algorithms that maintain perceptual quality without requiring the original high-frequency color data.
3Productivity
If the blue component is heavily subsampled to increase compression, then compression rate improves, but color accuracy may deteriorate
Solution Approach 1:
The invention introduces the H (hue) channel as an intermediary that carries color differentiation information. Instead of directly subsampling the blue channel in RGB space, the transformation to YHB color space creates an intermediate representation where color information is distributed across H and B channels. This intermediary structure allows for more efficient subsampling while maintaining color accuracy through the complementary information in the H channel.
Data Source
AI summary
An improved color space (YHB model) for compressing image files is provided. An example method includes storing a sum of an unweighted first color value and an unweighted second color value for each pixel in a plurality of pixels of an image as a first channel, sub sampling, among the plurality of pixels, a difference between the first color value and the second color value as a second channel, sub sampling, among the plurality of pixels, a third color value as a third channel, and storing the first channel, the second channel, and the third channel as the compressed image. In some implementations, the original image may be split into a low frequency version and a high frequency version. The system may apply the YHB model to the high frequency version and apply a conventional model or a second variation of the YHB model to the low frequency version.