Gaming Headset Video Reconstruction to Reduce Bandwidth
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Solution Overview
Problem
Existing VR and AR systems face challenges in achieving high-quality video output while maintaining portability due to the need for high bandwidth data transfer and interference from wireless data transmission mechanisms, and checkerboard rendering introduces visual artefacts.
Innovation Solution
A gaming headset equipped with a processing unit that receives incomplete video frame data, applies a machine learning algorithm to predict missing data, and combines it with received data to generate complete frames, reducing the amount of data transmission and improving image quality.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If uncompressed video data is transmitted from game console to VR headset, then video quality is improved, but bandwidth requirements increase
Solution Approach 1:
The patent extracts only a portion of the video frame data (e.g., every other line or specific regions) for transmission, rather than transmitting the complete uncompressed video data. This extraction approach reduces bandwidth requirements while maintaining acceptable video quality through predictive reconstruction of the missing portions.
Solution Approach 2:
The patent uses predictive algorithms to generate copies or approximations of the missing video frame data based on the transmitted portion. The processing unit in the VR headset reconstructs the incomplete video frames by predicting and filling in the missing portions, effectively creating a complete video frame from partial data.
2Manufacturing precision
If high bit rate data transfer is used, then video quality is improved, but device portability deteriorates
Solution Approach 1:
The patent extracts and transmits only essential portions of video frame data, reducing the computational burden on the headset and allowing for lighter hardware design. By transmitting less data, the headset requires less powerful (and therefore lighter) processing equipment to achieve high-quality video output.
Solution Approach 2:
The predictive reconstruction process occurs in the headset using algorithms that generate missing video data from the transmitted portions. This approach allows the headset to achieve high video quality without requiring proportionally high-power hardware, thereby reducing weight and improving portability.
3Weight of moving object
If standard wireless transmission is used, then device portability is improved, but data transmission reliability deteriorates
Solution Approach 1:
The patent transmits a reduced set of video frame data through wireless channels, which decreases the total data volume and reduces the probability of transmission errors. By transmitting fewer data elements, the system achieves more reliable data transfer over wireless connections while maintaining acceptable video quality through predictive reconstruction.
4Device complexity
If checkerboard rendering is used, then hardware requirements are reduced, but image quality deteriorates
Solution Approach 1:
Instead of using checkerboard rendering that creates visible artifacts, the patent employs predictive algorithms that generate smooth, continuous approximations of the missing video frame data. The processing unit predicts and fills in missing portions based on temporal and spatial correlations, producing high-quality reconstructed images without the characteristic checkerboard artifacts.
Solution Approach 2:
The patent changes the approach from spatial subsampling (checkerboard rendering) to temporal and contextual prediction. By using machine learning models and predictive algorithms that analyze motion patterns and scene continuity, the system reconstructs missing data with higher fidelity, improving image quality while maintaining reduced hardware requirements.
Data Source
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AI summary
A gaming headset comprising a processing unit configured to: receive incomplete video frame data, representing a first portion of a video frame; predict video frame data by applying a machine learning algorithm to the incomplete video frame data to generate predicted video frame data; combine the incomplete video frame data and predicted video frame data to generate complete video frame data representing a whole video frame; output the complete video frame data to a display.