Universal Codec for GPU Data Compression
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Solution Overview
Problem
Existing codec techniques are inefficient when applied universally to all types of data, leading to significant inefficiencies in compression and decompression processes, particularly in graphics processing units and central processor units, where different data types require tailored compression methods to optimize memory bandwidth usage and power consumption.
Innovation Solution
A universal codec that re-organizes data into a linear list, determines skip bits, clear masks, and constant masks, and calculates differences and differences-of-differences to adapt to various data types, using entropy encoding techniques to achieve efficient compression and decompression, thereby reducing memory bandwidth and power consumption.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If the same compression technique is applied to all data types, then the implementation is simplified and can be used universally, but the compression efficiency deteriorates significantly
Solution Approach 1:
The patent implements a universal codec structure that can handle multiple data types (depth, color, vertex data) through a common framework. The encoder and decoder use the same basic algorithmic approach, providing implementation simplicity and universality while maintaining the ability to adapt to different data characteristics through parameter adjustments and configuration options.
Solution Approach 2:
The patent applies different compression strategies and parameters for different data types within the universal framework. For example, depth data may use different prediction modes or entropy encoding parameters compared to color data, allowing each data type to be compressed with locally optimized quality while maintaining a single unified implementation.
2Quantity of substance
If data is compressed using traditional methods, then memory bandwidth usage is reduced, but power consumption remains high due to inefficient processing
Solution Approach 1:
The patent performs prediction and difference calculation operations before the main compression process. By pre-computing differences between consecutive values and organizing data in advance, the encoder reduces the complexity of subsequent entropy encoding steps, leading to fewer processing cycles and lower power consumption while achieving the same compression ratio.
Solution Approach 2:
The compression algorithm exploits the inherent structure and redundancy in the data itself without requiring external assistance or complex preprocessing. The difference encoding and entropy coding work directly on the input data patterns, allowing the system to achieve efficient compression with minimal additional hardware or power overhead.
3Productivity
If multiple specialized compressors are used for different data types, then compression efficiency is optimized, but device complexity increases
Solution Approach 1:
The patent implements a single universal codec that can compress depth data, color data, and vertex data using the same basic algorithmic structure. This eliminates the need for multiple specialized compressor modules, reducing device complexity while maintaining good compression efficiency through adaptive parameter selection and data-type-aware processing within the unified framework.
Data Source
AI summary
Techniques related to coding data including techniques for coding data using a universal codec are generally described. In some examples, such techniques may provide a universal (or unified) codec parameterized using a small set of parameters, which may be used to adapt the codec to different types of data to be compressed.