Real time human vision system behavioral modeling

• US 6,678,424 B1
• Filed: 11/11/1999
• Issued: 01/13/2004
• Est. Priority Date: 11/11/1999
• Status: Expired due to Term

• Current Assignee: Project Giants, LLC
• Original Assignee: Tektronix Incorporated (Fortive Corp.)
• Inventors: Ferguson, Kevin M.
• Primary Examiner(s): Chen, Wenpeng
• Application Number:
  US09/438,697
• Time in Patent Office: 1,524 Days
• Field of Search: 382/254, 382/260, 382/275, 382/274, 382/239, 382/240, 382/286, 348/189, 348/192, 348/180
• US Class Current: 382/286
• CPC Class Codes: H04N 17/00 Diagnosis, testing or measuring for television systems or their details

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. The variance of difference segmented processed image signal is normalized by the variance for the reference segmented processed image signal, and the Nth root of the result is taken as the measure of visible impairment of the test image signal. The measure of visible impairment may be converted into appropriate units, such as JND, MOS, etc.

Claims

1. A method of realtime human vision system behavioral modeling to obtain a measure of visible impairment of a test image signal derived from a reference image signal comprising the steps of:

filtering each image signal to produce respective processed image signals;

injecting a noise signal into each of the processed image signals;

calculating variances for the processed image signal representing the reference image signal and for a difference processed image signal representing the difference between the processed image signals from the injecting step; and

combining the variances to obtain the measure of impairment.

2. The method as recited in claim 1 wherein the combining step comprises the steps of:

normalizing the variance of the difference processed image signal using the variance of the reference processed image signal to produce a normalized variance for the difference processed image signal; and

taking the Nth root of the normalized variance as the measure of impairment.

3. The method as recited in claim 1 further comprising the step of converting the measure of impairment into appropriate units.

4. The method as recited in claim 1 wherein the filtering step comprises the steps of:

converting the reference and test image signals into respective luminance image signals;

performing a two-dimensional low-pass filter operation on the respective luminance image signals to produce the respective processed image signals.

5. The method as recited in claim 4 wherein the filtering step further comprises the steps of:

segmenting the respective processed image signals from the performing step into regions of similar statistics to produce segmented processed image signals;

obtaining block means for each of the segmented processed image signals; and

subtracting the respective block means from each pixel of the respective processed image signals to produce filtered image signals as the processed image signals for input to the injecting step.