Image Decoding Framework Using Heuristic Prioritization
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Solution Overview
Problem
Decoding images during download wastes CPU and memory resources, especially when images are not rendered or used, as existing technologies do not effectively differentiate between necessary and unnecessary image decoding.
Innovation Solution
Implementing a framework that delays image decoding based on heuristics and use history, prioritizing images that are likely to be rendered or used, decoupling image download from decoding to optimize resource utilization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If image decoding is performed at the time of download, then images are ready for immediate rendering, but CPU resources and memory are wasted when images are not rendered or used
Solution Approach 1:
The patent applies preliminary action by pre-decoding images that are likely to be used based on heuristics and use history, while delaying decoding of images that are less likely to be rendered. This selective preliminary decoding prepares critical images in advance without wasting resources on unnecessary decoding, thus resolving the contradiction between rendering readiness and resource efficiency
Solution Approach 2:
The system changes the timing parameter of image decoding from immediate (at download) to delayed or accelerated based on dynamic parameters such as use history, priority level, and likelihood of rendering. This parameter change allows the system to adapt decoding timing to actual usage patterns, improving resource efficiency while maintaining rendering performance
2Loss of energy
If image decoding is delayed based on heuristics and use history, then CPU resources and memory are utilized more efficiently, but images may not be ready when needed for rendering
Solution Approach 1:
The patent implements feedback mechanisms by monitoring image use history, rendering performance, and user behavior patterns. This feedback information is used to continuously refine decoding decisions, ensuring that images are decoded in advance with high probability before they are actually needed, thus maintaining rendering reliability while optimizing resource usage
Solution Approach 2:
By using heuristics and use history to identify images that are likely to be rendered, the system performs preliminary decoding actions on these specific images before they are actually needed. This targeted preliminary action ensures rendering availability is maintained for critical images while avoiding unnecessary decoding of non-critical images
3Reliability
If all downloaded images are decoded, then all images are available for rendering, but memory resources are consumed by decoding images that will never be used
Solution Approach 1:
The patent applies local quality by differentiating between critical and non-critical images based on their likelihood of being rendered. Critical images (those with high use history or priority) are decoded and stored in memory, while non-critical images are kept in compressed form. This local differentiation of quality ensures memory is used efficiently while maintaining availability of actually needed images
Data Source
AI summary
In one or more embodiments, a framework is provided in which image decoding can be delayed based on heuristics, and later initiated based on a use type associated with the image or the likelihood that the image is going to be used. For example, a use history and priority system can enable images that are currently being rendered and/or are likely to get rendered to be decoded before images that have a history of being downloaded but never used. Accordingly, by decoupling image download from image decoding, CPU resources and memory can be more efficiently utilized.