Texture Processing Method with Adaptive Data Loss Control
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Solution Overview
Problem
Existing texture processing methods face challenges in achieving precision enhancement while reducing power consumption, particularly in mobile devices with limited memory and resources, as data loss during compression and filtering can degrade image quality and increase power usage.
Innovation Solution
A computing system that determines and manages data loss amounts during texture compression and filtering processes, using information about first and second data loss amounts to optimize texture processing, allowing permissible data loss to maintain image precision and reduce power consumption.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Use of energy by moving object
If texture compression is performed to reduce memory bandwidth and power consumption, then power consumption and memory bandwidth are reduced, but data loss occurs during compression which degrades image quality
Solution Approach 1:
The system obtains information about the first data loss amount that occurred during texture compression, and uses this feedback to determine the second data loss amount allowable during texture filtering. This feedback mechanism allows the system to dynamically adjust filtering parameters based on actual compression loss, optimizing the balance between image quality and power consumption.
Solution Approach 2:
The system changes the parameter of allowable data loss amount during texture filtering based on the actual data loss from compression. By dynamically adjusting the second data loss amount parameter according to the first data loss amount measured during compression, the system optimizes the trade-off between maintaining image precision and reducing power consumption.
2Measurement precision
If texture filtering is performed with high precision to maintain image quality, then image precision is improved, but power consumption increases
Solution Approach 1:
The system dynamically changes the precision parameter during texture filtering by determining the second data loss amount allowable based on the first data loss amount from compression. This allows the system to adjust filtering precision adaptively, maintaining image quality when necessary while reducing power consumption when compression loss is already significant.
Solution Approach 2:
The system applies partial filtering action by allowing a controlled second data loss amount during texture filtering. Instead of performing full precision filtering always, the system selectively applies filtering with adjusted precision based on compression loss, achieving sufficient image quality with reduced computational effort and power consumption.
3Quantity of substance
If texture compression uses higher compression ratios to reduce memory bandwidth, then memory bandwidth and power consumption are reduced, but data loss increases which affects image quality
Solution Approach 1:
The system uses feedback from the compression process by obtaining information about the first data loss amount, and applies this feedback to adjust the filtering parameters. This allows the system to compensate for compression loss through adaptive filtering, enabling higher compression ratios while maintaining acceptable image quality.
Data Source
AI summary
A texture processing method and apparatus that obtains information about a first data loss amount that occurred during a texture compression process. A determination is made regarding a second data loss amount that allowable during a texture filtering process based on the obtained information regarding the first data loss amount. Texture filtering is then performed by using the second data loss amount. At least one processor determines the second data loss amount based on a difference between the third data loss amount and the first data loss amount.