Texture Compression via Rate Distortion Optimization and Match Chains
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Solution Overview
Problem
Current texture compression techniques in virtual environments face challenges in achieving a balance between compression ratio and quality, with existing methods often resulting in suboptimal storage efficiency and decompression times, particularly when using fixed-bitrate formats like BC1, BC3, BC4, BC5, and BC7.
Innovation Solution
A statistical approach employing a generic Rate Distortion Optimization (RDO) algorithm that encodes textures into multiple block bitstreams, detects collisions, and forms match chains to select the best encoding configurations, allowing for flexible tradeoffs between quality and size, and enabling compatibility with various lossy compression formats such as ASTC.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If fixed-bitrate compression formats (BC1, BC3, BC4, BC5, BC7) are used for texture compression, then the compression process is simple and fast, but the compression ratio is limited and storage efficiency is suboptimal
Solution Approach 1:
The texture is divided into multiple blocks, and each block is encoded into multiple candidate bitstreams with different distortion values. This segmentation allows the system to process textures in manageable units while generating multiple encoding variants, enabling subsequent selection of optimal encodings that balance compression ratio and quality.
Solution Approach 2:
The system changes the encoding parameters by generating multiple candidate bitstreams for each block with different distortion characteristics. This parameter variation allows the system to explore different compression quality levels and select the optimal trade-off between compression ratio and visual quality, overcoming the fixed-bitrate limitation.
2Manufacturing precision
If multiple encoding configurations are generated and evaluated, then the compression ratio and quality optimization is improved, but the processing time and computational complexity increase
Solution Approach 1:
Multiple candidate bitstreams are generated for each block in advance, with each encoding configuration associated with its distortion value. This preliminary generation of multiple encodings allows the system to prepare optimization candidates before final selection, enabling quality optimization without excessive real-time computational overhead.
Solution Approach 2:
The system evaluates distortion values associated with each candidate bitstream and uses this feedback to select the optimal encoding configurations. By measuring and comparing distortion metrics, the system can iteratively refine the selection process, ensuring high encoding quality while managing processing time through informed decision-making.
3Quantity of substance
If lossy compression is applied to reduce storage size, then the storage efficiency is improved, but the quality of texture data is degraded
Solution Approach 1:
Different encoding configurations are generated for different blocks based on their specific distortion characteristics and quality requirements. This local quality approach allows the system to apply appropriate compression levels to different regions of the texture, optimizing the balance between storage size and quality for each local area rather than applying uniform compression.
Solution Approach 2:
The system dynamically selects encoding configurations based on the trade-off between compression ratio and quality for each block. By adapting the encoding strategy to local requirements and available resources, the system can optimize storage efficiency while maintaining acceptable quality levels, making the compression process flexible rather than static.
Data Source
AI summary
Each of multiple blocks into which a texture has been divided is encoded into multiple block bitstreams. Each block bitstream corresponds to a respective encoding configuration. For each block, the block bitstreams having the lowest distortion values are selected such that up to a predetermined number of distinct block bitstreams are selected. For each selected block bitstream, data chunks are obtained such that each data chunk is a contiguous section of the respective selected block bitstream from which it is obtained. Data chunks of different sizes are obtainable. Collisions among the obtained data chunks are detected. Based on the detected collisions, match chains with redundant data chunks are formed such that, for each match chain, the redundant data chunks therein represent a common value of an associated block bitstream. The best match chains are selected. A lossy encoding of the texture is obtained from the selected best match chains.