Computational Observer for Human-Perceivable Image Quality Testing
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Solution Overview
Problem
Current objective testing systems for image quality evaluation fail to accurately account for display characteristics and human perception, leading to inefficiencies and inaccuracies in detecting visual artifacts.
Innovation Solution
A system and method that incorporate display and human physiological data, using a computational observer to process images through a display model and eye model, modifying images to simulate display and perception effects, and comparing these modifications to assess human-perceivable differences.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If subjective test scoring using human test subjects is used to evaluate image quality, then measurement precision is improved, but loss of time increases
Solution Approach 1:
The patent creates a computational model that copies and simulates human visual system characteristics including the optical transfer function of the human eye, cone absorption properties, and retinal processing. This computational observer reproduces human perception responses without requiring actual human subjects, thereby maintaining measurement precision while eliminating time loss.
Solution Approach 2:
The patent replaces the mechanical system of human subjective evaluation with an automated computational system that uses mathematical models to simulate human visual perception. This substitution transforms the evaluation process from a time-consuming human-centric approach to an efficient computer-based analysis that incorporates display characteristics and human perception models.
2Loss of time
If common objective testing systems using PSNR and pattern-color sensitivity are used, then loss of time is reduced, but measurement precision deteriorates
Solution Approach 1:
The patent changes the parameters used in objective testing from traditional PSNR and pattern-color sensitivity metrics to a comprehensive model that incorporates display spatial characteristics (subpixel configuration), spectral emission properties, and human visual system parameters (optical transfer function, cone absorption). This parameter transformation enables objective testing to achieve measurement precision comparable to subjective evaluation.
Solution Approach 2:
The patent creates a composite evaluation model that combines multiple previously separate components: display characteristics (subpixel arrangement, spectral emission), optical transfer function of the human eye, cone absorption characteristics, and retinal processing models. This composite approach integrates various factors into a unified objective testing framework that accurately predicts human perception.
3Device complexity
If objective testing systems do not incorporate display spatial characteristics and human perception characteristics, then device complexity is reduced, but measurement precision deteriorates
Solution Approach 1:
The patent creates a universal computational observer model that serves multiple functions: it evaluates image quality, simulates human visual perception, accounts for display characteristics, and predicts artifact visibility. This multi-functional model integrates display spatial characteristics, spectral properties, and human perception characteristics into a single comprehensive system that maintains measurement precision without proportionally increasing complexity.
Data Source
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AI summary
A system and method for image testing is configured to apply at least one display property to a test image to generate a display modified test image and applying the at least one display property to a reference image to generate a display modified reference image. The system also applies a human eye model to the display modified test image to generate an eye modified test image and applies the human eye model to the display modified reference image to generate an eye modified reference image. The system may compare the eye modified test image with the eye modified reference image to determine human perceivable differences between the test image and the reference image.