GAN Plugin Media Resolution Conversion Bandwidth Reduction
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Solution Overview
Problem
Network bandwidth limitations cause latency and buffering issues with playback of streamed media content in desired high-quality formats like HD, Full HD, Quad HD, and Ultra HD, due to higher file size requirements exceeding available bandwidth.
Innovation Solution
A computer-implemented method using Generative Adversarial Network (GAN) plugins to determine available device resources, select an appropriate GAN plugin, and estimate a lower resolution quality for media files, allowing conversion from the received lower quality to the desired output quality, thereby reducing bandwidth consumption.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If media files are transmitted at high resolution quality (HD, Full HD, Quad HD, Ultra HD), then the output quality is improved, but bandwidth consumption increases causing latency and buffering issues
Solution Approach 1:
A GAN plugin acts as an intermediary between the received low-resolution media file and the desired high-resolution output. The plugin performs super-resolution conversion by training a generative adversarial network model that maps low-resolution inputs to high-resolution outputs, effectively mediating the transformation while maintaining quality without requiring high bandwidth transmission
Solution Approach 2:
Instead of transmitting the actual high-resolution media file, the system transmits a low-resolution version and uses a GAN model to generate a synthetic copy that appears visually identical to the high-resolution original. This copying approach achieves the same visual outcome with significantly reduced bandwidth consumption
2Adaptability or versatility
If multiple GAN plugins with different network sizes are provided, then adaptability to different device resources is improved, but device complexity increases
Solution Approach 1:
The system dynamically selects appropriate GAN plugins based on real-time assessment of device resources such as memory, processing power, and available bandwidth. This dynamic adaptation allows the same streaming system to optimize performance across diverse device configurations without requiring manual configuration or complex fixed architectures
Solution Approach 2:
The GAN plugin collection is segmented into multiple versions with different network sizes and resource requirements. Each segmented plugin is optimized for specific device categories (e.g., mobile, desktop, high-end), allowing the system to select the most appropriate segment for each device without overwhelming complexity
Data Source
AI summary
A computer-implemented method comprises determining a desired resolution quality of a media file; determining available resources of a computing device; selecting one of a plurality of Generative Adversarial Network (GAN) plugins based on the determined available resources of the computing device; and estimating a second resolution quality of the media file corresponding to the selected GAN plugin. The second resolution quality is less than the desired resolution quality. The method further comprises communicating with a server to receive the media file at the estimated second resolution quality; converting the received media file from the second resolution quality to the desired resolution quality by using the selected GAN plugin; and outputting the converted media file with the desired resolution quality.