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23 June 2003 An objective method for combining multiple subjective data sets
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Proceedings Volume 5150, Visual Communications and Image Processing 2003; (2003)
Event: Visual Communications and Image Processing 2003, 2003, Lugano, Switzerland
International recommendations for subjective video quality assessment (e.g., ITU-R BT.500-11) include specifications for how to perform many different types of subjective tests. In addition to displaying the video sequences in different ways, subjective tests also have different rating scales, different words associated with these scales, and many other test variables that change from one laboratory to another (e.g., viewer expertise and criticality, cultural differences, physical test environments). Thus, it is very difficult to directly compare or combine results from two or more subjective experiments. The ability to compare and combine results from multiple subjective experiments would greatly benefit developers and users of video technology since standardized subjective data bases could be expanded upon to include new source material and past measurement results could be related to newer measurement results. This paper presents a subjective method and an objective method for combining multiple subjective data sets. The subjective method utilizes a large meta-test with selected video clips from each subjective data set. The objective method utilizes the functional relationships between objective video quality metrics (extracted from the video sequences) and corresponding subjective mean opinion scores (MOSs). The objective mapping algorithm, called the iterated nested least-squares algorithm (INLSA), relates two or more independent data sets that are themselves correlated with some common intermediate variables (i.e, the objective video quality metrics). We demonstrate that the objective method can be used as an effective substitute for the expensive and time consuming subjective meta-test.
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Margaret H. Pinson and Stephen Wolf "An objective method for combining multiple subjective data sets", Proc. SPIE 5150, Visual Communications and Image Processing 2003, (23 June 2003);

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