Index Compression for Fixed Block Texture Formats
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Solution Overview
Problem
The high cost and bandwidth requirements of storing and rendering true color images, particularly for real-time images and videos, due to the large amount of data required for each pixel, necessitate efficient compression and decompression methods to manage memory and bandwidth effectively.
Innovation Solution
A system that performs index compression and decompression by identifying tiles in an image, generating and storing indices with base and delta values, and using non-linear filtering to determine pixel color values, especially for pixels on edges, allowing for efficient storage and display of images without requiring large memory or bandwidth.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If true color information (32 bits per pixel) is stored for each pixel in an image, then image quality is maintained, but memory cost and bandwidth requirements become prohibitively high
Solution Approach 1:
The image is divided into tiles, and each tile is further segmented into multiple bands (e.g., 4 bands). Index values are stored at specific locations within each band rather than for every pixel, reducing the total number of stored values while maintaining reconstruction quality through interpolation between bands.
Solution Approach 2:
The patent introduces a band dimension in addition to the spatial dimensions. Instead of storing complete color information for every pixel in a single plane, the same pixel location is represented across multiple bands with different index precision levels, transforming the storage approach from spatial redundancy to dimensional distribution.
2Measurement precision
If true color information (32 bits per pixel) is transferred for real-time images and videos, then image quality is maintained, but bandwidth requirements become impossible to meet without increasing circuit layout size
Solution Approach 1:
The color information transfer is segmented into multiple bands with different precision requirements. Critical regions use higher precision bands while less critical regions use lower precision bands, reducing total data transfer volume and bandwidth requirements while maintaining perceptual image quality.
Solution Approach 2:
Different regions of the image are assigned different quality levels through the band structure. Important edges and features are preserved with higher precision in specific bands, while smooth regions use lower precision, optimizing bandwidth usage based on local image characteristics rather than uniform high precision everywhere.
3Measurement precision
If index values are stored at every pixel location, then color accuracy is improved, but compression efficiency deteriorates
Solution Approach 1:
The set of all pixel locations is segmented into multiple bands, where only certain bands store index values at every pixel location while other bands store index values at subsampled locations. This segmentation allows the system to achieve acceptable color accuracy with reduced storage by combining information from multiple bands.
Solution Approach 2:
Instead of storing complete color information for every pixel (excessive action), the patent stores partial color information at selected pixel locations across multiple bands. The missing information is reconstructed through interpolation, applying partial action that achieves sufficient accuracy without the overhead of complete storage.
Data Source
AI summary
A method for performing index compression. The method includes identifying a tile in an image, wherein the image comprises a plurality of tiles, wherein each tile includes color associated with a plurality of pixels. Furthermore, the method includes generating a plurality of indices located throughout the tile, and storing the plurality of indices. Additionally, the method includes offsetting zero or more locations of an index of the plurality of indices from a pixel location.