GPU Image Compression Modes for Guaranteed Bandwidth Reduction
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Solution Overview
Problem
Current data compression methods for image data in graphics processing units (GPUs) face challenges in balancing memory bandwidth and quality, especially in mobile devices where power consumption is a concern, and existing methods may not effectively guarantee a compression threshold, leading to inefficiencies in memory storage and transfer.
Innovation Solution
A lossy data compression method that divides image blocks into sub-blocks, analyzes alpha channel values to select from multiple compression modes, including fixed and variable alpha channel modes, ensuring a guaranteed compression threshold is met, thereby reducing memory bandwidth and storage requirements while maintaining random access capabilities.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If data compression is applied to reduce memory bandwidth and storage requirements, then memory bandwidth and storage needs are reduced, but the compression ratio may not be guaranteed and data quality may deteriorate
Solution Approach 1:
The patent divides image data into multiple blocks, and each block is further divided into sub-blocks. Different compression modes can be applied to different sub-blocks based on their local characteristics. This segmentation allows the system to guarantee compression ratios by selecting appropriate modes for each region while maintaining overall data quality.
Solution Approach 2:
The patent employs multiple compression modes (first compression mode with higher quality, second compression mode with lower quality) that can be dynamically selected based on local image characteristics such as alpha channel values. This dynamic adaptation allows the system to maintain quality where needed while achieving compression where possible, thereby guaranteeing overall compression ratios.
2Quantity of substance
If lossy compression is used to achieve higher compression ratios, then storage requirements are reduced, but data quality deteriorates
Solution Approach 1:
The patent applies different compression qualities to different regions of the image based on local characteristics. Specifically, sub-blocks with certain alpha channel characteristics (e.g., fully transparent or fully opaque) are compressed with higher quality or using specific modes, while other regions may use more aggressive compression. This local quality approach ensures that important regions maintain data quality while achieving overall storage reduction.
3Manufacturing precision
If multiple compression modes are implemented to guarantee compression threshold, then compression ratio guarantee is achieved, but device complexity increases
Solution Approach 1:
The patent performs preliminary analysis of image block characteristics (such as alpha channel values) before selecting compression modes. By pre-evaluating whether blocks contain transparent pixels, fully opaque regions, or other characteristics, the system can deterministically select appropriate compression modes. This preliminary action simplifies the decision-making process and reduces runtime complexity while ensuring compression thresholds are met.
4Use of energy by moving object
If compression is applied to reduce power consumption in mobile devices, then power consumption is reduced, but memory bandwidth efficiency may worsen due to decompression overhead
Solution Approach 1:
The patent applies compression selectively to only those image blocks that benefit from it, rather than compressing all data uniformly. By identifying blocks with specific characteristics (such as those with uniform alpha values or simple patterns) and applying compression only to these regions, the system reduces overall power consumption while minimizing decompression overhead. This partial action approach maintains memory bandwidth efficiency by avoiding unnecessary compression/decompression cycles.
Data Source
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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.