Alpha-Aware Pixel Block Compression for Guaranteed Random Access
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Solution Overview
Problem
Current data compression methods in graphics processing units (GPUs) face challenges in reducing memory bandwidth and storage space efficiently, especially when dealing with image data, due to varying compression ratios and the need for random access, which can lead to increased power consumption and latency.
Innovation Solution
A lossy data compression method that divides blocks of pixels into sub-blocks, analyzes alpha channel values to select from a set of candidate compression modes, including fixed and variable alpha channel modes, ensuring a guaranteed compression threshold is met, thereby reducing memory bandwidth and storage space while enabling random access.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Use of energy by moving object
If data compression is applied to reduce memory bandwidth and storage space, then power consumption is reduced, but compression ratio variability and random access requirements can increase power consumption and latency
Solution Approach 1:
The patent divides image data into multiple blocks and further subdivides each block into sub-blocks for independent compression analysis. This segmentation allows the system to apply different compression modes to different regions, enabling faster decompression of accessed regions while maintaining overall compression efficiency, thus reducing latency without sacrificing power savings.
Solution Approach 2:
The patent dynamically selects compression modes based on alpha channel analysis of each sub-block. By analyzing alpha values and selecting from multiple candidate compression modes, the system adapts to local data characteristics, optimizing the balance between compression ratio and decompression speed for random access patterns, thereby reducing both power consumption and latency.
2Quantity of substance
If lossy compression is applied to reduce data size, then storage space and memory bandwidth are reduced, but data quality is compromised
Solution Approach 1:
The patent applies different compression modes to different sub-blocks based on their alpha channel characteristics. Regions with uniform alpha values use more aggressive compression, while regions with varying alpha values use modes that preserve quality better. This local adaptation ensures data quality is maintained where needed while achieving maximum compression where possible, resolving the contradiction between data size reduction and quality preservation.
Solution Approach 2:
The patent changes compression parameters dynamically based on alpha channel analysis. By selecting from multiple candidate compression modes with different quality-compression trade-offs, the system adjusts compression intensity according to local data characteristics, achieving high compression ratios overall while maintaining acceptable quality in critical regions.
3Reliability
If multiple compression modes are used to guarantee compression threshold, then compression quality is improved, but device complexity increases
Solution Approach 1:
The patent segments the analysis into sub-block level operations, where simple alpha channel statistics are computed for each sub-block. This segmentation allows complex multi-mode selection to be broken down into simpler, localized decisions, reducing overall hardware complexity while still achieving reliable compression threshold guarantees through cumulative effects across multiple sub-blocks.
Solution Approach 2:
The patent performs alpha channel analysis at the sub-block level, which is a partial action compared to full-block analysis. This partial analysis provides sufficient information for mode selection without requiring complete characterization of the entire block, achieving compression threshold guarantees with reduced computational and hardware complexity.
Data Source
AI summary
Lossy methods and hardware for compressing data and the corresponding decompression methods and hardware are described. The lossy compression method comprises dividing a block of pixels into a number of sub-blocks and then analysing, for each sub-block, and selecting one of a candidate set of lossy compression modes. The analysis may, for example, be based on the alpha values for the pixels in the sub-block. In various examples, the candidate set of lossy compression modes comprises at least one mode that uses a fixed alpha channel value for all pixels in the sub-block and one or more modes that encode a variable alpha channel value.