Abstract: A method of predicting subjective quality ratings of processed video from corresponding human vision model perceptual difference scores obtains perceptual difference scores for a “Worst” quality video training sequence and for a “Best” quality video training sequence. Corresponding subjective quality rating values are assigned to the perceptual difference scores as modified by any single-ended measures of impairments that may exist in the reference video training sequences from which the “Worst” and “Best” quality video training sequences are derived.

Abstract: A human vision model based slow motion interpolation apparatus and method that renders smooth interpolated video at a desired rate from a slower rate video source up-samples the slower rate video to the desired rate. The up-sampled video is input to a human vision model based adaptive filter that has recursive characteristics. The output from the adaptive filter is the smooth interpolated video without a direct current component. A direct current restorer may be used to add to the smooth interpolated video the direct current component from the up-sampled video signal.

Abstract: Temporal processing for realtime human vision system behavioral modeling is added at the input of a spatial realtime human vision system behavioral modeling algorithm. The temporal processing includes a linear and a non-linear temporal filter in series in each of a reference channel and a test channel, the input to the reference channel being a reference image signal and the input to the test channel being a test image signal that is an impaired version of the reference image signal. The non-linear temporal filter emulates a process with neural attack and decay to compensate for a shift in peak sensitivity and for frequency doubling in a spatio-temporal sensitivity function. The linear temporal filter accounts for the remaining subtleties in the spatio-temporal sensitivity function.

Abstract: A variable sample rate recursive digital filter is an adaptive digital filter where its coefficients are recalculated for each sample rate being processed in such a way as to maintain a constant frequency rate for all sample rates. An equivalent resampling is done by taking the ratio of the bilinear transforms at the respective sample rates.

Abstract: An adaptive spatio-temporal filter for use in video quality of service instruments based on human vision system models has a pair of parallel, lowpass, spatio-temporal filters receiving a common video input signal. The outputs from the pair of lowpass spatio-temporal filters are differenced to produce the output of the adaptive spatio-temporal filter, with the bandwidths of the pair being such as to produce an overall bandpass response. A filter adaptation controller generates adaptive filter coefficients for each pixel processed based on a perceptual parameter, such as the local average luminance, contrast, etc., of either the input video signal or the output of one of the pair of lowpass spatio-temporal filters. Each of the pair of lowpass spatio-temporal filters has a temporal IIR filter in cascade with a 2-D spatial IIR filter, and each individual filter is composed of a common building block, i.e., a first order, unity DC gain, tunable lowpass filter having a topology suitable for IC implementation.

Abstract: A streaming media quality analyzer system compares original streaming media from a source, or original packetized streaming media from the source, with a reconstructed packetized streaming media representing the packetized streaming media as received over a network in a connectionless manner at a remote site. At the remote site the received packetized streaming media is analyzed to determine time of arrival of the packets including identification of missing packets. The analysis results are transmitted over the network to a measurement site and used to reconstruct the received packetized streaming media from the original packetized streaming media. The reconstructed packetized streaming media is then analyzed to determine the quality of the packetized streaming media received at the remote site.

Abstract: An automatic video signal identification system uses zero-mean normalized reference signals that are cross-correlated with an input video signal. The input video signal is stored in an acquisition memory and then cross-correlated with a plurality of reference signal templates, each template representing a particular video signal to be identified. The cross-correlation process provides a corresponding plurality of maximum correlation outputs. The largest maximum correlation output is determined, and the corresponding template identified. If not already normalized, the largest maximum correlation .output is normalized to produce a correlation coefficient. Based upon the identified template the correlation coefficient is tested to determine the validity of the match, i.e., whether the video signal is identified or whether the video signal is unknown.