Image Processing Effect Queue Reordering for CPU-GPU Data Exchange
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Solution Overview
Problem
Existing image editing applications face performance bottlenecks and excessive memory usage when providing undo, toggle, or modify functionality due to the need to reprocess all effects after changes, leading to processor constraints and inefficient data exchange between CPU and GPU.
Innovation Solution
An image editing application utilizing two processing units with different capabilities maintains a queue to reorder effects for efficient reprocessing, minimizing data exchanges by identifying impacted effects and offloading CPU-specific operations to the GPU, thereby reducing the need for reprocessing intermediate results.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If all effects are reprocessed from scratch after removing or modifying an effect, then the image can be accurately reproduced with the changed effects, but the processing time and computational resources increase significantly
Solution Approach 1:
The patent applies preliminary action by maintaining a cache of intermediate rendering results at multiple stages of the effects pipeline before final composition. When an effect is removed or modified, the system can retrieve previously computed intermediate results from the cache rather than reprocessing all effects from scratch, significantly reducing processing time while maintaining accurate image reproduction.
2Loss of time
If intermediate rendering results are cached for every effect stage, then reprocessing time is reduced, but memory consumption increases excessively
Solution Approach 1:
The patent applies local quality by selectively caching intermediate rendering results based on their potential usefulness. Instead of uniformly caching all intermediate results, the system identifies and caches only those intermediate results that are likely to be needed for future undo or modify operations, optimizing the balance between memory consumption and reprocessing time reduction.
Solution Approach 2:
The patent applies partial action by implementing a selective caching strategy that caches only a portion of intermediate results rather than all of them. The system determines which intermediate rendering stages are most valuable to cache based on the effects pipeline structure and usage patterns, achieving significant performance improvement without excessive memory consumption.
3Productivity
If data is continuously exchanged between CPU and GPU during effect reprocessing, then processing can be distributed across both units, but a bottleneck is created that reduces overall system performance
Solution Approach 1:
The patent applies segmentation by dividing the effects pipeline into distinct CPU-processing stages and GPU-processing stages, with clear boundaries between them. Intermediate results are cached at these boundaries, allowing each processing unit to work independently on its designated effects without requiring continuous data exchange, thereby eliminating the CPU-GPU bottleneck while maintaining high processing throughput.
Data Source
AI summary
Some embodiments provide an image editing application that utilizes at least two processing units to efficiently process an image with multiple effects after at least one of the effects modified from the image. Some such embodiments maintain a queue for identifying each of the effects to reprocess. Some embodiments determine a modified ordering for the effects in the queue based on criteria that is related to reducing data exchanges between the processing units when applying the effects. From the modified ordering, a first processing unit applies a first set of the effects before passing resulting data to a second processing unit for application of a second set of the effects. The second processing unit applies a second set of effects to produce the image with the multiple effects and the at least one effect removed or modified.