K2T Video Quality Measurement Algorithm
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing methods for measuring video quality degradation due to linear distortions in standard definition television (SDTV) are not applicable to other video formats like high definition television (HDTV) and computer video, as they fail to account for differences in bandwidth and human vision sensitivity.
Innovation Solution
A K2T measurement algorithm that uses a single variable function of the video format to determine a normalized graticule, adjusting the K2T measurement to match human vision masking models for various formats, ensuring consistent quality assessment across different video formats.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If the standard definition graticule is simply scaled using bandwidth ratios for high definition formats, then the measurement can be applied to new formats, but the measurement does not track visual sensitivity to impairments
Solution Approach 1:
The patent applies local quality by making the graticule non-uniform across different spatial frequency regions. Instead of simple uniform scaling, the graticule is designed with different amplitude characteristics at different frequencies to match the human vision system's varying sensitivity. The graticule amplitude is adjusted locally at each frequency point based on vision masking models, allowing high definition measurements to accurately reflect visual sensitivity while maintaining format versatility.
2Adaptability or versatility
If the graticule is re-scaled according to line time, then the measurement adapts to different formats, but the re-scaled graticule does not correspond to the difference in test signal spectrum
Solution Approach 1:
The patent applies parameter changes by adjusting the graticule amplitude parameter as a function of spatial frequency rather than using fixed time-based scaling. The graticule is defined by parameters that vary with frequency to match the spectral characteristics of test signals in different formats. This allows the graticule to correspond accurately to the test signal spectrum while adapting to high definition formats, resolving the mismatch between simple time-scaling and spectral correspondence.
Data Source
AI summary
K2T measurement of video signals, regardless of video format, is determined by generating a graticule using an algorithm having a single variable that is a function of the video format for the particular video signal. The graticule is set to a normalized value of one for a range of +/−T, where T is a function of the video format. The graticule is set to a normalized value over a range of +/−(1.5T to flatBreak) according to a human vision masking model algorithm expressed as K+C/t, where K is a constant K factor, t is a function of T, and C is the single variable that is a function of the video format. The normalized value of the graticule beyond a point designated by flatBreak is a constant=K. Finally a linear curve fit between one and K+C/t over the range of +/−(T to 1.5T) sets the remaining values for the graticule.