PQA600A Datasheet, PDF (9/19 Pages) List of Unclassifed Manufacturers – Picture Quality Analysis System

Picture Quality Analysis System — PQA600A
Auto spatial alignment execution with spatial region of interest selected
Automatic Temporal/Spatial Alignment
The PQA600A supports automatic temporal and spatial alignment, as well
as manual alignment.
The automatic spatial alignment with spatial region of interest in Measure
mode selected independently of the spatial alignment function can measure
the cropping, scale, and shift in each dimension, even across different
resolutions and aspect ratios (for example, when aligning SD to HD video).
If extra blanking is present within the standard active region, it is measured
as cropping when this function is enabled.
The automatic spatial and temporal alignment allows picture quality
measurement between reference and test videos of different resolutions
and frame rates.
Region of Interest
There are two types of spatial/temporal Region of Interest (ROI): Input and
Output. Input ROIs are used to eliminate spatial or temporal regions from
the measurement which are not of interest to the user. For example, Input
Output Spatial ROI on Review mode for in-depth investigation
Spatial ROI is used when running measurements for reference and test
videos which have different aspect ratios. Input Temporal ROI, also known
as temporal sync, is used to execute measurements just for selected frames
and minimize the measurement execution time.
Output ROIs can be used to review precalculated measurement results
for only a subregion or temporal duration. Output Spatial ROI is instantly
selected by mouse operation and gives a score for just the selected spatial
area. It's an effective way to investigate a specific spatial region in the
difference map for certain impairments. Output Temporal ROI is set by
marker operation on the graph and allows users to get a result for just a
particular scene when the video stream has multiple scenes. It also allows
users to provide a result without any influence from initial transients in the
human vision model. Each parameter can be embedded in a measurement
for the recursive operation.
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