Image Data Confidence Scoring for Rendering Efficiency
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Solution Overview
Problem
Current computer graphics processing technologies are inefficient in generating and storing data for image frames, particularly when multiple graphical objects are repeated across frames, leading to redundant data generation and storage.
Innovation Solution
A data processing apparatus and method that includes a processing unit, memory, detection circuitry, prediction circuitry, and allocation circuitry to generate and store image data for graphical objects based on confidence scores indicating the likelihood of reuse across multiple frames, allowing for efficient storage and retrieval of frequently used object data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If image data for multiple graphical objects is generated for each image frame, then complete and accurate rendering data is achieved, but redundant data generation occurs when objects are repeated across frames
Solution Approach 1:
The system performs preliminary actions by generating image data for graphical objects and storing it in memory before it is needed for subsequent frames. The confidence score mechanism predicts which objects are likely to appear in future frames, allowing the system to pre-generate and store their image data, thus avoiding redundant generation when the objects actually appear.
Solution Approach 2:
The system creates copies of image data for graphical objects in memory based on predicted confidence scores. Instead of regenerating data for repeated objects, the system retrieves stored copies from memory, significantly reducing the computational overhead of generating identical data multiple times while maintaining rendering quality.
2Reliability
If all generated image data is stored in memory, then data availability for rendering is ensured, but memory resources are wasted on data that will not be reused
Solution Approach 1:
The system applies local quality by differentiating the treatment of different graphical objects based on their individual characteristics and predicted importance. Instead of uniformly storing all generated data, the confidence score mechanism identifies which specific objects warrant storage in memory, allocating memory resources selectively to high-value data while discarding or not storing low-value data.
Solution Approach 2:
The system changes the parameter of data retention by introducing confidence scores that dynamically determine whether generated image data should be stored in memory. This parameter-based approach allows the system to adapt memory usage based on predicted object importance, transforming a static all-or-nothing storage approach into a dynamic, selective storage strategy.
3Manufacturing precision
If image data is generated and stored for every object in every frame, then rendering accuracy is maintained, but processing time increases due to redundant operations
Solution Approach 1:
The system performs preliminary generation and storage of image data for objects predicted to appear in future frames, based on confidence scores derived from object properties. This preliminary action shifts processing work to when objects are first encountered rather than repeating generation for each frame, maintaining rendering accuracy while significantly reducing redundant processing time.
Solution Approach 2:
The system implements feedback through the confidence score mechanism, which analyzes object properties and rendering history to predict future object appearances. This feedback loop allows the system to learn from past frames and optimize data generation and retrieval strategies, maintaining accuracy while minimizing processing time through intelligent prediction.
Data Source
AI summary
A data processing apparatus comprises a processing unit to generate image data for rendering data for display as an image frame, a memory to store the image data, the image data comprising data for a given object in the image frame, detection circuitry to detect, for the data for the given object, one or more properties associated with the given object in the image frame, prediction circuitry to generate, based on one or more of the properties, a confidence score for the data for the given object indicative of a likelihood that the data for the given object is to be used for more than a threshold number of image frames in a sequence of image frames, and allocation circuitry to allocate indicator data to the data for the given object, the indicator data indicative of a magnitude of the confidence score for the data for the given object, and to store the data for the given object in the memory in association with the corresponding indicator data.