Abstract:
An apparatus and method for predicting human vision perception and perceptual differences provides a perceptual difference model that merges two human vision systems, one for a reference video signal and the other for an impaired version of the reference video signal. The respective video signals are processed by spatio-temporal filters and, prior to differencing, by noise masking modules that apply a noise mask as a function of pupil size and luminance. The differenced filtered signal is an initial perceptual difference map to which other masking is applied to take into account correlation and contrast gain based upon the noise masks and filtered luminance from the spatio-temporal filters. The result is a more precise output perceptual difference map.

Description:
BACKGROUND OF THE INVENTION 
   The present invention relates to video quality of service, and more particularly to an improvement in predicting human vision perception and perceptual difference for video quality metrics using masking to take into consideration pupil size variation and other effects. 
   The problem addressed by the present invention is predicting subjective quality ratings of video that has been processed in such a way that visually detectable impairments may be present. Human vision perceptual models have been shown to be instrumental in the solution to this problem by predicting perceptibility of impairments. See U.S. Pat. No. 6,678,424 entitled “Realtime Human Vision System Behavioral Modeling” and U.S. Patent Application Publication No. 2003/0053698 A1 entitled “Temporal Processing for Realtime Human Vision System Behavior Modeling”, both by the present inventor. These documents describe two merged human vision systems, one for a reference video signal and the other for an impaired video signal, as a perceptual difference model. The model has filter pairs, each having a two-dimensional lowpass filter and an implicit high pass filter, and the outputs are differenced to produce an impaired image map which is further processed to produce a measure for picture quality of the impaired video signal relative to the reference video signal. The filters are primarily responsible for variations in human vision response over spatial and temporal frequencies—spatiotemporal response. Such filters may be adaptive, as described in pending U.S. Patent Application Publication Nos. 2002/0186894 A1 entitled “Adaptive Spatio-Temporal Filter for Human Vision System Models” and 2003/0031281 A1 entitled “Variable Sample Rate Recursive Digital Filter”, both by the present inventor. The adaptive filters have two paths, a center path and a surround path, for processing an input video signal, each path having a temporal and spatial component. A controller determines adaptively from the input video signal or one of the path outputs the coefficients for the filter components. The difference between the path outputs is the adaptive filter output for the input video signal. 
   The filters take into account most of the effects of lens and pupil related optical modulation transfer function (MTF), lateral inhibition, aggregate temporal response of photoreceptors, neurons, etc., and adaptation of pupil, neurons, including dark adaptation, etc. Although this model does change spatial and temporal frequency response due to pupil changes, it does not take into consideration the effects of noise masking changes due to pupil changes in response to changes in luminance, and does not consider other adaptation due to similarity (correlation) between test and reference signals, luminance sensitivity including the equivalent of luminance portion of contrast gain control or spatiotemporal effects of the variance or AC portion of contrast gain control. 
   What is desired is an improved method of masking for predicting human vision perception and perceptual difference in order to produce a more accurate prediction of subjective quality ratings of video. 
   BRIEF SUMMARY OF THE INVENTION 
   Accordingly the present invention provides a method and apparatus for predicting human vision perception and perceptual difference by merging two human vision systems into a perceptual difference model that includes noise masking based on photon noise and other masking for correlation, luminance sensitivity and variance effects. Each human vision system has a spatio-temporal filter that processes the respective reference and impaired video signals to provide filtered outputs that are differenced to obtain an initial perceptual difference map. Prior to the differencing a noise masking module is applied to each filter output which takes into account pupil size vs. luminance. The noise masking modules perform a photon noise amplitude estimation based on a filtered luminance output from the filters, the resulting noise signal being used to mask the filtered output signal to produce the filtered outputs that are differenced. The initial perceptual difference map may then be subjected to other masking for correlation and contrast gain based on the filtered luminance outputs, the noise masking outputs and various masking parameters to produce an output perceptual difference map and derivative summary measures. The perceptual difference model summary measures are calibrated to adjust the various masking parameters by applying known stimuli from the literature used for subjective human vision modeling and comparing the results with the literature results. The parameters are adjusted to produce an approximate match with the literature results in response to many stimuli. 
   The objects, advantages and other novel features of the present invention are apparent from the following detailed description when read in conjunction with the appended claims and attached drawing. 

   
     BRIEF DESCRIPTION OF THE SEVERAL VIEWS OF THE DRAWING 
       FIG. 1  is a block diagram view of a perceptual difference model according to the present invention. 
       FIG. 2 . is a block diagram view of a noise masking module for the perceptual difference model according to the present invention. 
       FIG. 3  is a block diagram view of an area threshold module for the perceptual difference model according to the present invention. 
   

   DETAILED DESCRIPTION OF THE INVENTION 
   Referring now to  FIG. 1  a perceptual difference model with masking  10  is shown using two merged human vision systems. Each human vision system receives a video input signal, either a reference video signal or an impaired video signal, that is processed by respective adaptive filters  12 ,  14 , which filters may be like those described in the above-mentioned pending U.S. Patent Application Publication No. 2002/0186894 A1 entitled “Adaptive Spatio-Temporal Filter for Human Vision System Models” (the filter adaptation controller not being shown). The respective outputs of the filters  12 ,  14  are input to respective noise masking modules  16 ,  18  together with an output of the filters that is predominately average or lowpass (temporally and spatially) luminance in nature. The outputs of the noise masking modules  16 ,  18  are input to a subtractor  20  to produce an “AC Difference” signal, which is an initial perceptual difference map. The local mean or lowpass luminance filter output from the filters  12 ,  14 , the output from the noise masking modules  16 ,  18  and the AC Difference signal are input to an Other Masking module  22 , as described further below. 
   One of the noise masking modules  16 ,  18  is shown in further detail in  FIG. 2 . The output from the filter  12 ,  14  that is predominately lowpass and bandpass luminance in nature is input to a photo noise amplitude estimation module  24  to produce a noise signal N representative of photon noise, which is described further below. The output from the filter  12 ,  14  that is the filtered output S is input to a first squarer  26 , while the noise signal N is input to a second squarer  28 . The outputs from the squarers  26 ,  28  are summed ( 30 ) and then input to a square root module  32 . The noise signal N is negated ( 34 ) and input to a summer  36  together with the output from the square root module  32 . The resulting output from the summer  36  is the square root of the sum of the squares less the noise signal, i.e., Y={SQRT(S 2 +N 2 )−N}. The sign of the input filtered signal S is extracted ( 38 ) and used to multiply ( 40 ) the output of the summer  36  to produce a resulting signal from the noise for input to the subtractor  20  and the Other Masking module  22 . 
   Photon noise is a function of pupil diameter and retinal illuminance. Pupil diameter and retinal illuminance are interdependent, and solving for both as a function of the luminance of an image that reaches the pupil involves in a recursive manner matching the relationship of retinal illuminance vis a vis luminance with the relationship of pupil size vis a vis retinal illuminance using data points taken from literature, such as “Vision: Human and Electronic” by Albert Rose, David Sarnoff Research Center, RCA, Plenum Press, NY/London, 1973, page 30. Based upon this process it was determined that the photon noise may be approximated with a simple piecewise exponential:
 
NoiseAmpMode(lum):=if(lum&lt;1, lum y1 , lum y2 )*K
 
where K:=6.2 y 1 :=0.375 y 2 :=0.285. K represents photon noise sensitivity. Therefore the photon noise amplitude estimation module  24  uses the above equation to determine from the filtered luminance signal the noise signal N. Since this equation is based upon the pupil size, the noise signal N also takes into account pupil size.
 
   The Other Masking module  22 , as shown in  FIG. 1 , takes the absolute sum ( 42 ) and product ( 44 ) of the lowpass filtered luminance signal from the two human vision systems and applies respective weighting functions w 3 , w 0  ( 46 ,  48 ) to produce “DC Sum” and “DC Product” signals. Likewise the outputs from the respective noise masking modules  16 ,  18  are also absolutely summed ( 50 ) and multiplied together ( 52 ). The absolute value of the summed output of the noise masking modules  16 ,  18  is weighted ( 51 ) by a weighting factor w 4 , which may be omitted if w 4 =1, and then ( 54 ) by the DC Sum as well as being input to a two-dimensional lowpass filter  56 , the lowpass filter having a given gain w 2 . The product output of the noise masking modules  16 ,  18  is input to another two-dimensional lowpass filter  58  having a given gain w 1 , the output of which is converted ( 60 ) to an absolute value as a local “Correlation” signal. The DC Product, the DC Sum, the Correlation and the weighted AC Sum signals are summed ( 62 ) and input to an inverse square root module  64  having a given weighting factor w 5 . The DC Sum signal and the output from the impaired lowpass filter  56  are input to an area threshold module  66  which compensates for the size of the area over which an impairment is discerned. The AC Difference signal, the output from the inverse square root module  64  and the output from the area threshold module  66  are multiplied together ( 68 ) to produce an output perceptual difference map. 
   As shown in  FIG. 3  in the area threshold module  66  the DC Sum signal is clamped ( 70 ) to provide an absolute value. The absolute DC Sum signal and the AC Sum signal from the 2-D lowpass filter  56  are input to a comparator  72  and to a calculation module  74 . In the comparator  72  the AC Sum signal is compared to the DC Sum signal as modified by a threshold weight (TW), and in the calculation module  74  the quantity ACSum/(TW*DCSum) is computed. The output from the comparator  72  controls a switch  76  which has as inputs a logical “1” and the output from the calculation module  74 . The output from the switch  76  is “1” if ACSum≧DCSum*TW and is ACSUM/(TW*DCSum) if ACSum&lt;DCSUM*TW. TW is the inverse of the gain w 2  of the 2-D lowpass filter  56 . 
   The next step is to calibrate the perceptual difference model to determine appropriate values for the various parameters. This includes parameters for the filters  12 ,  14 , for the noise masking modules  16 ,  18  and for the other masking module  22 . Particularly for the other masking module  22 , w 0  represents luminance product masking weight, w 3  represents luminance sum masking weight, w 1  represents reference and impaired local correlation masking weight, w 2  represents filtered AC threshold for unaffected due to area, and w 5  represents overall system gain. Other parameters are: for correlation masking a local correlation lowpass filter pole, for supra-threshold “self-masking” a reference and test contrast envelope masking weight w 4  (set to one for this example); and for area thresholding a local reference and impaired contrast envelope lowpass filter pole. For the calibration a number of simulations are performed to assess how well the perceptual difference model behavior matches human subjective behavior recorded in various experiments documented in the literature. Experiments are chosen based on the combination of two factors:
         the deviation from transparent (linear, all pass) response shown   the relevance of stimulus response for video applications under consideration.       

   Although there is a great deal of overlap, classes of experiments may be roughly categorized as
         spatiotemporal contrast threshold   contrast discrimination and other “masking” related   spatial supra-threshold   temporal supra-threshold       

   Each of these may vary any or all of the related parameters described above in combination with luminance, size, contrast and duration of target and/or masker. The threshold data is used to calibrate the perceptual difference model for approximately least mean square error (LMSE) in response relative to 0.1, per M. Cannon, “A Multiple Spatial Filter Model for Suprathreshold Contrast Perception” in  Vision Models for Target Detection and Recognition , ed. Eli Peli (World Scientific Publishing, River Edge, N.J. 1995), pages 245–283. The spatial supra-threshold data are used to calibrate the model for approximately the values given in the Cannon reference, while equal contrast contours of Eli Peli et al, “Contrast Perception Across Changes in Luminance and Spatial Frequency” J. Opt. Soc. Am., October 1996, Vol. 13, No. 10 pages 1953–1959, are matched against equivalent contrast from the Cannon reference. The temporal supra-threshold data is relative and at this time is normalized and checked for correlation, as no common scale related to other data sets was determined by Lawrence E. Marks “Apparent Depth of Modulation as a Function of Frequency and Amplitude of Temporal Modulations of Luminance” J. Opt. Soc. Am. July 1970, Vol. 60, No. 7, pages 970–977. Calibration is an iterative process. Model responses for all the calibration stimuli are compared against the ideal responses from the literature. Calibration parameters are adjusted to minimize error. 
   In other words reference and/or impaired video signals used by the researchers reporting in the literature in determining human visual perception responses are applied to the perceptual difference model and the output is compared with the reported results of the human subjective tests. The parameters are adjusted to achieve a minimum LSME. This is repeated for other reference and/or impaired video signals used by other researchers. After many iterations the set of parameters that produces the minimum LSME across all the stimuli tests is determined to be those for use in the perceptual difference model. Where the literature does not provide both reference and impaired video signals, an appropriate match to the supplied video signal is generated so the perceptual difference model has both reference and impaired video signal inputs. The match is typically a constant or “flat field” at the same average luminance level. 
   Thus the present invention provides an apparatus and method for predicting human vision perception and perceptual difference that uses a perceptual difference model which merges two human vision systems, one for a reference video signal and the other for an impaired version of the reference video signal, uses a noise masking module as part of the human vision system that takes into account pupil size (photon noise), and uses other masking to account for correlation between reference and impaired video signals as well as the components of contrast gain control, the various parameters for the perceptual difference model being calibrated using human subjective test data from existing literature in an iterative process.