Color Look Up Table Compression Using Lossy DCT and Corrective Data
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Solution Overview
Problem
Color management systems face challenges in efficiently storing and processing large color tables due to increasing memory requirements, especially in devices with finer sampling and larger bit depths, leading to increased costs and memory consumption.
Innovation Solution
A method for compressing color tables using lossy compression techniques, such as discrete cosine transform (DCT), to reduce the size of the color tables while maintaining acceptable color differences within a defined error threshold, and storing the compressed data on memory devices like print cartridges, with corrective information to reconstruct accurate color transformations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If color tables are stored with finer sampling and larger bit depths to improve color accuracy, then color management precision is improved, but memory consumption increases
Solution Approach 1:
The color table is divided into multiple segments or regions, with different compression techniques applied to different segments. Critical regions (e.g., neutral axis, skin tones) use lossless or low-compression methods to maintain accuracy, while less critical regions use higher compression ratios, thereby balancing color accuracy with memory efficiency
Solution Approach 2:
The patent transforms color table data from its original format into a compressed domain using mathematical transformations (e.g., discrete cosine transform). By changing the representation parameters of the color data, the system achieves significant compression while maintaining the ability to reconstruct accurate color values within acceptable error thresholds
2Quantity of substance
If lossy compression is applied to reduce color table size, then memory usage is reduced, but color accuracy deteriorates
Solution Approach 1:
Different regions of the color table are assigned different quality levels. Critical regions such as the neutral axis (grayscale values) and regions corresponding to human skin tones are preserved with high or lossless quality, while other regions tolerate higher compression artifacts. This selective quality approach maintains perceptual color accuracy while achieving overall compression
Solution Approach 2:
The system incorporates error threshold checking and corrective information storage. After compression, the system identifies regions where color errors exceed acceptable thresholds and stores corrective data for those specific regions. During decompression, these corrections are applied to restore accuracy in critical areas, providing feedback-based quality control
3Quantity of substance
If high compression ratios are used to minimize memory footprint, then storage efficiency is improved, but color reconstruction accuracy worsens
Solution Approach 1:
The compressed color table system uses a composite structure combining multiple data representations: compressed color table data, corrective information for error-prone regions, and metadata about the compression applied. This composite approach allows the system to achieve high overall compression ratios while maintaining reconstruction accuracy through the integrated corrective components
Data Source
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AI summary
A print cartridge includes a memory device with data to reconstruct a color table for a printing device. The data include a compressed bitstream stored in the memory device with a plurality of quantized coefficients from a lossy compression of a plurality of difference nodes of a difference color table at a selected step size. The value of each node in the difference color table is to be added to the corresponding node in the value of a corresponding node of a reference table stored on the printing device. Corrective information for a set of nodes to be modified with residual values is included. The quantized coefficients and the corrective information are compressed. The step size information is also included.