Color Tinting Component Mapping for Image Storage
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Solution Overview
Problem
Existing methods for representing and storing digital images are inefficient, particularly when dealing with color images, as they require significant memory and do not allow for flexibility in image generation, leading to potential loss of image quality due to palette size limitations.
Innovation Solution
The use of color tinting, where a color card applies component-to-color mapping to multiple component images, allowing for the creation, storage, and manipulation of images by combining these components to form a final tinted image, reducing memory requirements and enhancing flexibility in image generation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If palettes or color lookup tables are used to reduce per-pixel bit requirements, then memory usage per pixel is reduced, but image quality is lost due to folding pixel values and limited color range
Solution Approach 1:
The image is divided into multiple component images (e.g., luminance, saturation, hue channels) that are stored separately. Each component can be represented with fewer bits since it contains only a portion of the full color information. When needed, these components are recombined to reconstruct the full-color image, thereby reducing memory usage while preserving image quality.
Solution Approach 2:
Instead of reducing color depth in each pixel (moving from 24-bit to fewer bits per pixel), the patent moves to another dimension by storing multiple lower-bit component images. The full color information is recovered by combining these components, effectively trading spatial/temporal redundancy for color information preservation.
2Manufacturing precision
If multiple images are stored separately with full color information, then image quality is maintained, but memory usage increases significantly
Solution Approach 1:
Multiple related images are merged into a single composite data structure containing shared components. For example, multiple images with similar luminance or structural information store their common parts once, while only storing the differing color or detail components separately. This reduces total memory usage while maintaining the quality of each individual image when reconstructed.
Solution Approach 2:
The component images serve multiple functions simultaneously. A single set of luminance or structural components can be reused across multiple different color variations or image versions, making the storage system universal and efficient for handling families of related images.
3Quantity of substance
If palette size is limited to reduce memory, then memory efficiency improves, but color accuracy and image detail are compromised
Solution Approach 1:
Different components of the image are allocated different bit depths based on their importance and information content. For example, luminance information might be stored with higher precision than saturation, or certain frequency components might retain more detail than others. This local differentiation optimizes memory usage while preserving color accuracy where it matters most.
Data Source
AI summary
In an image processor, images are created, stored, manipulated and regenerated using color tinting, where color tinting applies component-to-color mapping from a color card to a plurality of component images, which are then combined to form a final image, tinted according to the content of the color card. In some instances, the color card might code one color for each of N components, in which case the final image might be the merging of each of N monochromatic component images colored by the color coded by the color card. In other instances, the color card codes for intensity levels or a texture.