Graphics Processor Validation Reference Image Selection
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Solution Overview
Problem
Manual reference image selection in graphics processor validation becomes impractical with large sets of images, leading to inefficiencies in validating graphics processor designs.
Innovation Solution
A system and method for automatically selecting a reference image from a set of reference images based on parameters such as color format, depth format, and anti-alias mode, and comparing the test image to the selected reference image to determine validation, with options for different selection modes and threshold settings.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual reference image selection is used, then validation can be performed with small sets of images, but it becomes impractical and inefficient with large sets of images
Solution Approach 1:
The system performs self-service by automatically selecting reference images based on parameters extracted from test images. The validation system no longer requires manual intervention for reference image selection, as it autonomously matches test images with appropriate reference images using parameter comparison and hashing mechanisms.
Solution Approach 2:
The manual mechanical process of selecting reference images is replaced with an automated computational system. The system uses parameter extraction, hashing algorithms, and database queries to automatically match test images with reference images, substituting human operation with mechanical/computational processes.
2Adaptability or versatility
If the set of reference images is expanded to cover more test cases, then validation coverage improves, but manual selection and management becomes more difficult
Solution Approach 1:
Instead of managing unique reference image files, the system creates parameter-based copies or representations through hashing. Each reference image is characterized by extracted parameters that serve as a digital fingerprint, allowing the system to manage and retrieve images based on their parameter signatures rather than physical file handling.
Solution Approach 2:
The system transforms the management approach from file-based to parameter-based. By extracting and hashing key parameters (color format, depth format, anti-alias mode, etc.), the system creates a parameter space that automatically organizes the reference image set, making management scalable regardless of set size.
3Productivity
If automatic reference image selection is implemented, then validation efficiency with large image sets improves, but system complexity increases
Solution Approach 1:
The automatic selection system is segmented into distinct functional modules: parameter extraction module, hashing module, database query module, and image retrieval module. Each module performs a specific function, making the overall complex system manageable through functional decomposition and independent optimization of each segment.
Data Source
AI summary
A system, method, and computer program product are provided for validating a graphics processor design. In operation, a test image is identified. Additionally, a reference image is automatically selected from a set of reference images. Furthermore, a graphics processor design is validated using the test image and the selected reference image.