Lossless Color Data Compression via Predictor Map
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Conventional graphics processors compress color data, resulting in loss of original data and visual artifacts during decompression.
Innovation Solution
A system and method for lossless compression of color data using a predictor map that selects an anchor sample and computes difference samples from neighboring samples, truncating and encoding these to determine if compression can be performed without loss, ensuring the original data is preserved.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of energy
If conventional compression is applied to color data, then bandwidth requirements are reduced, but data loss and visual artifacts occur
Solution Approach 1:
The color data is divided into packets of multiple sub-pixel samples. Each packet is processed independently through the compression algorithm, allowing selective application of compression techniques to different segments while maintaining overall data integrity through the lossless guarantee.
Solution Approach 2:
The patent transforms color data into difference samples by subtracting predicted values from actual values. This parameter transformation enables more efficient encoding while maintaining lossless compression by preserving the exact mathematical relationship that can be reversibly transformed back to original color data.
2Quantity of substance
If compression is applied to reduce data size, then storage bandwidth is improved, but decomposition reliability deteriorates
Solution Approach 1:
The compression algorithm uses a predictor map that references previously processed samples within the same packet to predict current sample values. This feedback mechanism creates redundancy that can be exploited for compression while maintaining the ability to exactly reconstruct original values through the reversible difference calculation.
Solution Approach 2:
The patent introduces difference samples as an intermediary representation between original color data and compressed storage format. These difference samples serve as a reliable intermediate state that preserves all information needed for exact reconstruction while enabling more efficient compression encoding.
3Loss of information
If lossless compression is implemented, then data integrity is maintained, but compression complexity increases
Solution Approach 1:
The predictor map is pre-configured based on the sampling format of the graphics surface before compression begins. This preliminary setup identifies anchor samples and their relationships in advance, allowing the compression algorithm to proceed systematically without complex real-time decision-making, thereby reducing operational complexity while maintaining lossless integrity.
Data Source
AI summary
One embodiment of the present invention sets forth a technique for lossless compression of color data. Color data for a packet including multiple sub-pixel samples is compressed using a predictor map that is selected based on the sampling format specified for the graphics surface storing the color data. The predictor map defines one of the samples as an anchor that is represented exactly and a transform indicating which neighboring samples are used to compute difference samples for the other samples in the packet. The difference samples are truncated and tested to determine if the difference samples can fit into one or more compressed data formats, i.e., if the color data can be compressed without loss. When compression can be performed without loss, the transformed packet is output. Otherwise, the original packet is output.