Real-time Mask Quality Predictor for Volumetric Video
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Solution Overview
Problem
Current image segmentation technologies face challenges in automatically and efficiently evaluating the quality of segmentation masks in real-time, particularly in resource-constrained environments and volumetric broadcasting, where manual evaluation is time-consuming and impractical.
Innovation Solution
A real-time mask quality predictor (MQP) module utilizing a neural network is developed to assess mask quality without a reference mask, trained on a dataset of images with varying quality and corresponding scores, enabling real-time quality scoring and adjustment in image processing applications.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual evaluation methods are used to assess mask quality, then measurement precision can be maintained, but productivity deteriorates due to time-consuming processes
Solution Approach 1:
The patent replaces manual mechanical evaluation processes with an automated neural network-based quality predictor. The MQP module processes mask images through trained neural networks to automatically generate quality scores, eliminating the need for manual inspection while maintaining assessment accuracy and dramatically improving evaluation speed for real-time applications.
2Measurement precision
If complex quality assessment algorithms are implemented, then measurement precision improves, but device complexity increases
Solution Approach 1:
The patent introduces a trained neural network as an intermediary component between the mask image input and quality assessment output. This intermediary has been pre-trained on diverse datasets to capture complex quality patterns, allowing the system to achieve high measurement precision through a standardized, manageable architecture rather than requiring complex custom algorithms for each assessment task.
3Productivity
If real-time processing is implemented, then productivity improves, but use of energy increases in resource-constrained environments
Solution Approach 1:
The patent performs preliminary actions by pre-training the neural network models offline on comprehensive datasets before deployment. This pre-training phase captures complex quality patterns in advance, allowing the deployed MQP module to perform rapid real-time assessments with reduced computational overhead during actual operation, thereby lowering energy consumption in resource-constrained environments while maintaining real-time performance.
Data Source
AI summary
An embodiment of an image processing apparatus may comprise one or more processors, memory coupled to the one or more processor to store image and mask data, and logic coupled to the one or more processors and the memory, the logic to capture a volumetric broadcast video signal in real-time and generate a sequence of frame images from the captured real-time volumetric broadcast video signal, segment an input image, which corresponds to a single frame of the sequence of frame images, to generate a mask image associated with the input image, and determine a mask quality score based on the input image and the associated mask image in real-time. Other embodiments are disclosed and claimed.