Hybrid Image Processing Pipeline for Parallel Tile Rendering
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Solution Overview
Problem
Existing image processing systems face limitations in handling large images due to performance penalties from intermediate results being written to disk and difficulties in concurrently processing smaller chunks, especially in push models, and added complexities in pull models for managing concurrency.
Innovation Solution
A hybrid image processing model that combines push and pull models by flattening the image processing chain, creating a token queue, and constructing a pipeline to enable parallel processing of tiles, allowing for efficient memory usage and concurrent processing while managing concurrency through a sink-centric approach.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If a push model is used to process large images, then all source data can be pushed from source to sink, but intermediate results need to be written to disk incurring performance penalties and increased hard drive usage
Solution Approach 1:
The patent divides the image processing into independent tile segments that can be processed in parallel. Each tile is a self-contained unit that can be processed independently, allowing multiple tiles to be processed simultaneously without requiring sequential disk I/O operations, thus improving throughput while reducing performance penalties.
Solution Approach 2:
The patent introduces a spatial dimension to processing by dividing the image into a grid of tiles. This allows the system to process multiple regions of the image simultaneously in different processing pipelines, effectively adding parallelism across the spatial dimension rather than sequentially processing the entire image.
2Productivity
If sequential processing is used from source to sink, then the push model is simple to implement, but it is difficult to concurrently process smaller chunks to optimize performance
Solution Approach 1:
The patent introduces an intermediary tile cache that sits between the source and sink, managing the concurrency of tile processing. This intermediary structure handles the complexity of parallel processing, thread management, and data flow coordination, allowing the system to achieve performance optimization without requiring complex custom concurrency management code throughout the entire processing pipeline.
3Quantity of substance
If a pull model is used to process image data in response to requests, then memory footprint is reduced by using smaller chunks, but additional complexities are added for managing concurrency
Solution Approach 1:
The patent creates a universal tile processing framework that handles both memory efficiency and concurrency management through a single unified architecture. The tile-based approach with centralized cache management provides a multi-functional solution that simultaneously reduces memory footprint by processing smaller chunks and simplifies concurrency management through a standardized interface for all processing operations.
4Ease of operation
If the push model is used, then all image data can be processed from source to sink, but it does not work well for interactive applications requiring responsive output
Solution Approach 1:
The patent performs preliminary actions by pre-processing and caching tiles in advance before they are actually needed by the sink. This allows interactive applications to request and display tiles immediately without waiting for the entire image processing pipeline to complete, improving responsiveness while maintaining high throughput through parallel processing of remaining tiles.
Data Source
AI summary
A hybrid processing-based image processing system performs image chain flattening, token queue creation and pipeline prior to image rendering in order to allow various portions of the image processing to be performed in parallel. Tokens are passed between the filters of the pipeline generated from the flattened image chain to allow order-preserving operations to be performed which result in the same image as would have been produced by the original image chain using sequential processing.