Video quality metrics (VQM) have often been evaluated and compared using simple measures of correlation with observers. This approach does not fully take into account the variability implicit in the observers. We present techniques for determining the statistical resolving power of a VQM, defined as the minimum change in the value of the metric for which subjective test scores show a significant change. These techniques have been applied to the VQEG dataset, and incorporated into the recent ATIS/T1A1 series of technical reports (TR), which provide a comprehensive framework for characterizing and validating full-reference video quality metrics (VQM). These approved TR, while not standards, will enable the USA telecommunications industry to incorporate video quality metrics into contracts and tariffs for compressed video distribution. New methods for assessing VQM accuracy and cross-calibrating VQM are an integral part of the framework. These methods have been applied to two VQM at this point: PSNR and the version of Sarnoff's JNDmetrix tested by VQEG. The framework is readily extensible to additional VQM.

For several useful tasks in photogrammetry and in model-based vision, noniterative methods that require only the inversion of systems of linear equations are developed. The methods are based on the theory of projective invariants. The tasks addressed are resection, intersection, and transfer, or model matching (with or without ground control points). The following kinds of transfer are examined: (a) coplanar object points (transfer to image 2 done using four reference points in image 1); (b) stereo camera system (transfer to stereo camera pair 2 done using four reference points in stereo pair 1); (c) general multicamera configuration (transfer of a ninth point to image 3 done using eight tie points in images 1 and 2).>