Graphics Data Compression via Entropy Parameter Division
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Solution Overview
Problem
Graphics processing units (GPUs) face challenges in efficiently managing memory bandwidth due to the high dynamic range (HDR) format of intermediate render targets, leading to increased memory consumption during rendering processes.
Innovation Solution
A system and method for compressing and decompressing graphics data by calculating differences between neighboring pixels, predicting values, calculating prediction errors, mapping these errors to positive values, and dividing them by an entropy parameter to reduce redundancy and optimize bit lengths, thereby reducing memory bandwidth consumption.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If graphics data is stored in high dynamic range (HDR) format, then rendering quality is improved, but memory bandwidth consumption increases
Solution Approach 1:
The pixel block is divided into multiple quadrants, and each quadrant is further divided into groups. This segmentation allows independent encoding of different regions, enabling more efficient compression by exploiting local correlations and reducing the overall memory bandwidth requirement while maintaining HDR rendering quality.
Solution Approach 2:
The patent transforms the graphics data by calculating prediction errors and mapping them to positive values. This parameter transformation reduces the dynamic range of the data and enables more efficient entropy encoding, thereby reducing memory bandwidth consumption while preserving the HDR rendering quality through reversible transformations.
2Productivity
If graphics data values are distributed about zero to minimize redundancy, then compression efficiency is improved, but data transformation complexity increases
Solution Approach 1:
The patent performs preliminary actions by calculating prediction errors and mapping them to positive values before the actual compression encoding. This preliminary transformation simplifies the subsequent entropy encoding process and improves compression efficiency, while the structured approach to these transformations keeps the implementation complexity manageable.
3Loss of information
If prediction errors are mapped to positive values and divided by entropy parameter, then bit length optimization is achieved, but encoding complexity increases
Solution Approach 1:
The patent applies parameter changes by dividing the mapped prediction errors by an entropy parameter to optimize bit lengths. This transformation concentrates the probability distribution, allowing more efficient entropy encoding. The structured approach to selecting and applying the entropy parameter keeps the encoding complexity manageable while achieving significant bit length optimization.
Data Source
AI summary
Methods and systems for data compression and decompression in a graphics processing system are provided. For example, in at least one embodiment, a method comprises distributing the graphics data values of a pixel block about zero to minimize redundancy, and the pixel block includes a plurality of quadrants. The method further comprises determining whether to encode the distributed graphics data values, and responsive to a determination to encode the distributed graphics data values, encoding at least one graphics data value of one of the quadrants depending on an encoding indicator. The encoding includes determining an entropy parameter and dividing each positive data value by the entropy parameter yielding an entropy part and a noise part.