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The first Modelfest group publication appeared in the SPIE Human Vision and Electronic Imaging conference
proceedings in 1999. "One of the group's goals is to develop a public database of test images with threshold data from
multiple laboratories for designing and testing HVS (Human Vision Models)." After extended discussions the group
selected a set of 45 static images thought to best meet that goal and collected psychophysical detection data which is
available on the WEB and presented in the 2000 SPIE conference proceedings. Several groups have used these datasets
to test spatial modeling ideas. Further discussions led to the preliminary stimulus specification for extending the database
into the temporal domain which was published in the 2002 conference proceeding.
After a hiatus of 12 years, some of us have collected spatio-temporal thresholds on an expanded stimulus set of
41 video clips; the original specification included 35 clips. The principal change involved adding one additional spatial
pattern beyond the three originally specified. The stimuli consisted of 4 spatial patterns, Gaussian Blob, 4 c/d Gabor
patch, 11.3 c/d Gabor patch and a 2D white noise patch. Across conditions the patterns were temporally modulated over
a range of approximately 0-25 Hz as well as temporal edge and pulse modulation conditions. The display and data
collection specifications were as specified by the Modelfest groups in the 2002 conference proceedings.
To date seven subjects have participated in this phase of the data collection effort, one of which also
participated in the first phase of Modelfest. Three of the spatio-temporal stimuli were identical to conditions in the
original static dataset. Small differences in the thresholds were evident and may point to a stimulus limitation. The
temporal CSF peaked between 4 and 8 Hz for the 0 c/d (Gaussian blob) and 4 c/d patterns. The 4 c/d and 11.3 c/d Gabor
temporal CSF was low pass while the 0 c/d pattern was band pass.
This preliminary expansion of the Modelfest dataset needs the participation of additional laboratories to
evaluate the impact of different methods on threshold estimates and increase the subject base. We eagerly await the
addition of new data from interested researchers. It remains to be seen how accurately general HVS models will predict
thresholds across both Modelfest datasets.
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