Texture Map Compression Using Derived Precision Bits
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Conventional real-time compression techniques for normal maps in computer graphics result in a loss of precision during data reconstruction, leading to reduced image quality.
Innovation Solution
A method that reduces N+1 bits to N bits, stores them in a specific order within a compression block, and derives a bit value to reconstruct the data with improved precision without storing an extra precision bit, effectively using a 'virtual' bit to enhance image quality.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If conventional real-time compression techniques are used to reduce data size, then the compression ratio is improved, but the precision of reconstructed normal vectors deteriorates
Solution Approach 1:
The patent applies dimensionality change by transitioning from storing N+1 bit values directly to storing N-bit values with a derived bit. The derived bit is reconstructed during decompression by analyzing the relationship between adjacent N-bit values, effectively adding a dimension of computational derivation to recover the precision information without storing it explicitly.
Solution Approach 2:
The patent changes the parameter representation by modifying how normal vector components are encoded. Instead of using fixed N+1 bit representations, the system uses N-bit values with a dynamically derived bit that changes based on the local variation between adjacent texels. This parameter transformation allows the same storage size to represent precision information adaptively.
2Measurement precision
If N+1 bit values are stored to maintain precision, then the precision of normal vectors is improved, but the memory usage and data transmission requirements increase
Solution Approach 1:
The patent extracts the least significant bit information from the N+1 bit representation and derives it computationally during decompression rather than storing it. By taking out this redundant bit and reconstructing it from the relationship between adjacent N-bit values, the system reduces storage requirements while maintaining the precision that would otherwise require the extra bit.
Solution Approach 2:
The patent creates a virtual copy of the precision information through derivation. Instead of copying the actual N+1 bit value, the system copies the essential N-bit information and generates the missing bit as a derived copy based on local context, achieving space-efficient representation without losing precision information.
3Quantity of substance
If compression is applied to reduce data size, then storage efficiency is improved, but the image quality deteriorates
Solution Approach 1:
The patent introduces dynamic adaptation into the compression scheme by making the derived bit value dependent on the local characteristics of adjacent texels. The compression approach dynamically adjusts based on the variation between neighboring values, applying higher effective precision where needed and maintaining compression efficiency where variation is low, thus preserving image quality throughout the rendered surface.
Data Source
AI summary
Methods and systems for compressing and decompressing data are described. A first value of N+1 bits and a second value of N+1 bits are reduced to strings of N bits each. The first and second strings of N bits are stored in a particular order relative to one another in a compression block. The particular order in which the first and second strings of N bits are stored in the compression block is used to derive a bit value that is then used in combination with one of the strings of N bits to reconstruct that string as N+1 bits.