Image Decimation Controller for Multiviewer GPU Memory Optimization
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Solution Overview
Problem
Multiviewer devices face performance bottlenecks due to increased memory loading when displaying multiple content streams, as existing GPUs struggle with data processing efficiency, leading to slow I/O and reduced application performance.
Innovation Solution
A system and method that includes a multiviewer with a graphics processing unit (GPU) and an image decimation controller, which determines if a content stream's tile is above a threshold and performs decimation by removing predetermined lines from each frame before loading into GPU memory, optimizing memory load and bandwidth.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If the number of tiles concurrently displayed in a multiviewer display is increased, then the display capability and information presentation are improved, but the total image dataset size increases causing memory loading constraints and GPU performance degradation
Solution Approach 1:
The patent extracts and removes redundant or less important pixel data from the image dataset before loading it into GPU memory. By selectively removing unnecessary visual information while preserving essential content, the system reduces the total data volume that needs to be processed, thereby alleviating memory loading constraints and improving GPU performance when displaying multiple tiles concurrently.
Solution Approach 2:
The patent applies different quality levels to different regions of the displayed content. Instead of uniformly processing all pixels at full resolution, the system identifies and processes only the most visually important regions at high quality while reducing or removing data from less critical areas. This localized quality approach maintains display capability while significantly reducing the total image dataset size.
2Productivity
If more content streams are processed concurrently, then the productivity and functionality of the multiviewer are improved, but the I/O speed becomes slower causing bottleneck in overall application performance
Solution Approach 1:
The patent performs preliminary decimation of image data before it is loaded into GPU memory. By pre-processing and reducing the data volume in advance, the system minimizes the I/O operations required during concurrent content stream processing. This preliminary action ensures that even as productivity increases with more concurrent streams, the I/O bottleneck is mitigated because less data needs to be transferred and processed.
3Manufacturing precision
If the data load to GPU is increased to maintain full resolution for all tiles, then the image quality is preserved, but the memory bandwidth consumption increases causing performance bottleneck
Solution Approach 1:
The patent changes the parameter of image data resolution selectively across different tiles and regions. Instead of maintaining uniform full resolution for all content streams, the system adjusts the resolution parameter based on tile importance, size, and visual significance. This parameter change reduces memory bandwidth consumption while preserving image quality where it matters most, eliminating the performance bottleneck caused by excessive data loading.
Data Source
AI summary
A system is provided for displaying content streams on a multiviewer device and includes a GPU that resizes image data loaded therein for display in a multiviewer tile, and an image decimation controller that performs a decimation of a content stream by removing a predetermined number of lines from each frame of the content stream before loading the content stream to the internal memory of the GPU for resizing and display on the multiviewer tile. In this system, the content stream is loaded to a full resolution buffer in system memory if the content stream is not a proxy stream of the media content and the tile designated for the content stream is less the predetermined threshold, such that the image decimation controller performs the decimation to delete the predetermined number of lines before the content stream is loaded in the internal memory of the GPU as the image data.