Image Data Conversion via Color Partitioning for Display Compatibility
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Solution Overview
Problem
Existing image processing technologies face challenges in converting image data from higher color bit numbers to lower bit numbers, particularly for display devices like MIP displays that can only handle lower color bits, resulting in data reduction and loss of image details.
Innovation Solution
A method and apparatus that acquire image data, divide the color value interval into partitions, and convert pixel values into lower bit representations by determining position-based second color values, reducing the number of bits required while retaining image details.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If image data is converted from higher color bit numbers to lower bit numbers, then the compatibility with display devices is improved, but the loss of image details increases
Solution Approach 1:
The color value interval is segmented into multiple color value partitions, where each partition corresponds to a specific second color value. This segmentation allows the conversion process to preserve important color transitions by identifying gray-scale transition boundaries that separate distinct color regions, thereby reducing information loss during bit number reduction.
Solution Approach 2:
The method changes the parameter representation by converting color values from a continuous high-bit representation to a discrete low-bit representation based on their position within color value partitions. This parameter transformation maintains essential visual information by mapping color values to partitions based on gray-scale transition boundaries, achieving compatibility while preserving image details.
2Quantity of substance
If the number of color bits is reduced, then the data size is decreased, but the color precision is degraded
Solution Approach 1:
By segmenting the color value interval into meaningful partitions based on gray-scale transitions, the method reduces data size through lower bit representation while maintaining color precision for visually significant color changes. Each partition represents a meaningful color step, preserving perceptual color precision despite reduced bit depth.
Solution Approach 2:
The method applies different treatment to different regions of the color value range by identifying gray-scale transition boundaries. Color values near transition boundaries are preserved with higher fidelity, while values within stable regions can be more aggressively quantized, achieving local optimization of color precision throughout the data range.
Data Source
AI summary
Disclosed is a method for converting image data. The method includes: acquiring image data of a target image, wherein the image data includes first pixel values of m pixels in the target image, each of the first pixel values includes a first color value of at least one color channel, the first color value being within a target color value interval of the at least one color channel; dividing the target color value interval into n color value partitions; determining a color value partition where the first color value falls from the n color value partitions; and converting the first color value into a second color value according to a position of the first color value in the color value partition, the number of bits occupied by the second color value being less than the number of bits occupied by the first color value.