Property modeling for real time human being visual system

A performance, normalization technique, used in television, image analysis, instrumentation, etc., to solve problems such as lack of instantaneous effects of spatial frequency sensitivity, model adoption, and obvious complexity

Inactive Publication Date: 2005-01-05
TEKTRONIX INC
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Problems solved by technology

However, this model was not adopted into the later full HVS model apparently because the further added complexity was too obvious
[0024] The temporal effects on spatial frequency sensitivity are mostly missing in these models
[0025] Finally, the effects of orientation and surrounding fields are only represented to the extent of quadrature filter and cross-pyramid level masking, which generally do not match well with real stationary data

Method used

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  • Property modeling for real time human being visual system
  • Property modeling for real time human being visual system

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Embodiment Construction

[0036] When examining the performance of the human visual system revealed by data from the literature in conjunction with historical data on the effects of various noise sources on the human visual system, the following observations can be made:

[0037]1. In the higher spatial frequency range, the amplitude sensitivity in the linear region of the influence of brightness on the contrast sensitivity function is insensitive to the changing average brightness, but decreases according to the square of the frequency. This corresponds to a second order low pass filter.

[0038] 2. Weber and Devries-Rose areas [see Taylor et al., "Contrast Detection and Discrimmation for Visual Models" and N. Gray Graham, and "Visual pattern Analyzers", Oxford U. press 1989] both correspond to the low frequency range where amplitude sensitivity is most affected by varying average luminance. Examination of the amplitude versus frequency plots at various mean luminances reveals behavior similar to a v...

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Abstract

A method of realtime human vision system modeling to produce a measure of impairment of a test image signal derived from a reference image signal processes the two signals in respective channels. The signals are converted to luminance image signals and low-pass filtered in two dimensions. The processed image signals are then segmented and block means values are obtained which are subtracted from the pixels in the corresponding processed image signals. Noise is injected into the segmented processed image signals and a variance is calculated for the reference segmented processed image signal and also for the difference between the segmented processed image signals.

Description

technical field [0001] The present invention relates to video image quality assessment, and more particularly to real-time human visual system performance modeling for producing objective measurements to predict subjective error assessments in video image signals. Video image signals are recorded and transmitted by lossy methods such as video compression. Background technique [0002] Approaches employing models of the human visual system for predicting observer subjective responses to errors introduced into video image signals subjected to lossy processing such as video compression include computationally expensive models of the human visual system (HVS) such as the following article Described by: J. Lubin, "A Visual Discrimination Model for Imaging System Design and Evaluation (for Imaging System Design and Evaluation of Visual Discrimination Model)", VisionModels for Target Detection and Recognition (for Target Detection and Confirmation Vision Model), World Scientific P...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06T7/00H04N17/00
CPCH04N17/00G06T7/00
Inventor K·M·菲尔古森
Owner TEKTRONIX INC
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