Coverage Compaction for Frame Buffer Storage Optimization
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Solution Overview
Problem
Conventional frame buffer compression methods are inefficient in storing coverage information, requiring a large number of bits to store coverage masks for each object, which limits storage efficiency and bandwidth for graphics data.
Innovation Solution
A method that sorts coverage masks by the descending number of samples covered and compacts additional masks by removing samples covered by previous masks, allowing for more efficient storage by using a coverage vector or compacting masks based on object coverage, thereby reducing the number of bits required.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If conventional frame buffer compression methods store coverage masks for each object, then complete coverage information is preserved, but storage efficiency decreases and bandwidth is consumed
Solution Approach 1:
The patent merges multiple coverage masks into a single compacted coverage mask by combining the coverage information of multiple objects. Instead of storing separate coverage masks for each object, the invention creates one consolidated coverage mask that represents the union of all object coverages, significantly reducing the quantity of coverage data stored in the frame buffer.
Solution Approach 2:
The compacted coverage mask serves multiple functions: it represents the coverage of all objects simultaneously, enables depth testing for multiple objects, and provides a unified coverage representation that can be used for various graphics processing operations. This single structure replaces multiple separate coverage masks while maintaining all necessary functionality.
2Reliability
If coverage masks are stored for each object, then accurate depth testing is enabled, but bandwidth usage increases
Solution Approach 1:
The patent combines multiple object coverage masks into a single compacted coverage mask that is used for depth testing of all objects. This merged coverage representation maintains the accuracy needed for reliable depth testing while reducing the bandwidth required to transfer coverage data from the frame buffer to the depth buffer.
Solution Approach 2:
The invention extracts only the essential coverage information needed for depth testing by creating a compacted coverage mask that contains the union of all object coverages. This extracted representation removes redundant coverage data while preserving the critical information needed for accurate depth testing operations.
3Loss of information
If full coverage masks are stored, then object coverage detail is maintained, but storage capacity for other graphics data is reduced
Solution Approach 1:
The patent merges multiple coverage masks into a single compacted representation that maintains all necessary object coverage details in a space-efficient format. This consolidation reduces the storage space required for coverage information, thereby increasing the available storage capacity for depth and color values in the frame buffer.
Solution Approach 2:
The invention changes the parameter representation of coverage data by transforming multiple detailed coverage masks into a single compacted coverage mask with a different data structure. This parameter change reduces the storage requirements while preserving the essential coverage information needed for graphics rendering operations.
Data Source
AI summary
A method for compressing graphics data, the method comprising sorting a plurality of coverage masks into an order of descending number of samples covered by the plurality of coverage masks. A first coverage mask is identified. The first coverage mask comprises a greatest number of covered samples. Additional coverage masks of the plurality of coverage masks are compacted in the order of descending number of samples covered. Compacting additional coverage masks comprises removing samples from the coverage mask that are covered by any other compacted coverage mask.