Digital Image Layer Prioritization for Network Loading
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Solution Overview
Problem
Loading digital images on computer networks is inefficient due to increased file sizes, leading to high resource consumption and noticeable loading times, especially with high-resolution images, which burdens bandwidth and computing resources.
Innovation Solution
The method involves determining areas of interest within an image, extracting features, parsing the image into layers, and prioritizing these layers for download, allowing the most important sections to be loaded first, thereby reducing overall loading time and conserving network resources.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If digital images are transmitted in full resolution, then image quality is improved, but network bandwidth consumption increases and loading time increases
Solution Approach 1:
The image is divided into multiple layers based on depth information, where each layer represents a different depth range. This segmentation allows the system to transmit only the most relevant layers first (those containing areas of interest), reducing initial bandwidth consumption while maintaining the ability to transmit full-resolution data if needed for complete image quality.
Solution Approach 2:
The system performs preliminary processing to identify areas of interest and depth layers before transmission. By pre-segmenting the image into depth-based layers and identifying which layers contain important content, the system can prioritize transmission of critical layers first, improving perceived loading speed without sacrificing overall image quality.
2Measurement precision
If digital images are transmitted in full resolution, then image quality is improved, but loading time increases
Solution Approach 1:
The image is divided into multiple layers based on depth information, where each layer represents a different depth range. This segmentation allows the system to transmit only the most relevant layers first (those containing areas of interest), reducing initial bandwidth consumption while maintaining the ability to transmit full-resolution data if needed for complete image quality.
Solution Approach 2:
The system performs preliminary processing to identify areas of interest and depth layers before transmission. By pre-segmenting the image into depth-based layers and identifying which layers contain important content, the system can prioritize transmission of critical layers first, improving perceived loading speed without sacrificing overall image quality.
3Productivity
If image processing is performed to identify areas of interest, then resource utilization is improved, but processing complexity increases
Solution Approach 1:
The system introduces depth information as an intermediary to simplify the areas of interest identification process. Instead of performing complex semantic analysis to identify important regions, the system uses depth maps to automatically segment the image into foreground and background layers, reducing processing complexity while still achieving effective resource utilization.
Data Source
AI summary
A digital image is stored on a server. One or more areas of interest are determined within the digital image. One or more sets of features for each of the one or more areas of interest within the digital image are extracted. The digital image is parsed into a set of image layers, wherein a subset of image layers is associated with a first set of features of the one or more sets of features. A request to download the image from the server is received from a client. The subset of image layers is sent to the client. In response to sending the subset, the remainder of the set of images is sent.