GUI Rendering Layout for Higher Compression and Lower Resource Use
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Solution Overview
Problem
Existing graphical user interface (GUI) rendering techniques consume excessive processing and memory resources, particularly in devices with limited power sources, leading to reduced device performance and user experience due to secondary graphics rendering issues.
Innovation Solution
Implementing a method and system that utilizes machine learning models to generate alternative GUIs with higher compression ratios by rearranging graphical elements, adjusting their size, position, and color, and applying aggressive lossy compression techniques to reduce resource usage while maintaining visual quality.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If aggressive lossy compression techniques are applied to GUI rendering, then processing and memory resources are reduced, but visual quality and rendering precision deteriorate
Solution Approach 1:
The display area is divided into multiple blocks, and each block is processed independently with compression. This segmentation allows the system to apply lossy compression at the block level while maintaining overall visual quality, as individual blocks are smaller and can be compressed more effectively without noticeable artifacts.
Solution Approach 2:
Different compression strategies are applied to different regions of the display based on their importance and content characteristics. Critical UI elements and high-attention areas use higher quality rendering, while less important background areas use more aggressive compression, optimizing the balance between resource usage and visual quality.
2Manufacturing precision
If more processing resources are allocated to GUI rendering, then visual quality improves, but device performance and battery life deteriorate
Solution Approach 1:
The system applies compression selectively to different portions of the GUI rather than uniformly processing all pixels at full quality. By using partial action (compressing only certain blocks or regions), the system reduces overall processing requirements and energy consumption while maintaining acceptable visual quality for the most important display elements.
3Quantity of substance
If high compression ratios are applied to GUI, then memory usage is reduced, but visual artifacts and quality loss increase
Solution Approach 1:
The display is segmented into multiple blocks that can be compressed independently. This allows the system to achieve higher overall compression ratios by processing smaller blocks, reducing memory usage while limiting the visibility of compression artifacts within each individual block.
Solution Approach 2:
The system dynamically adjusts compression parameters such as block size, compression level, and quality thresholds based on the specific GUI content and device capabilities. By changing these parameters adaptively, the system optimizes the balance between memory usage and visual quality for different rendering scenarios.
Data Source
AI summary
A system and method for generating a graphical user interface (GUI) is presented. The method includes receiving a plurality of graphical user interface (GUI) objects, each GUI object including an associated action; receiving a constraint for a first GUI object of the plurality of GUI objects; generating, using first machine learning model, a first GUI based on the plurality of GUI objects and the received constraint; determining compressibility of the first GUI; generating, using second machine learning model, a second GUI based on the plurality of GUI objects and the received constraint; determining compressibility of the second GUI; and generating an instruction to render only the first GUI, in response to determining that the compressibility of the second GUI is lower than the compressibility of the first GUI.