PQA600A Datasheet, PDF (6/19 Pages) List of Unclassifed Manufacturers – Picture Quality Analysis System

Datasheet
Attention Map Example: The jogger is highlighted
Attention Model
The PQA600A Opt. BAS and Opt. ADV or PQASW Opt. ADV, also
incorporate an Attention Model that predicts focus of attention. This model
considers:
Motion of Objects
Skin Coloration (to identify people)
Location
Contrast
Shape
Size
Viewer Distraction due to Noticeable Quality Artifacts
These attention parameters can be customized to give greater or less
importance to each characteristic. This allows each measurement using an
attention model to be user-configurable. The model is especially useful to
evaluate the video process tuned to the specific application. For example, if
the content is sports programming, the viewer is expected to have higher
attention in limited regional areas of the scene. Highlighted areas within the
attention image map will show the areas of the image drawing the eye's
attention.
Artifact Detection
Artifact Detection reports a variety of different changes to the edges of the
image:
Loss of Edges or Blurring
Addition of Edges or Ringing/Mosquito Noise
Rotation of Edges to Vertical and Horizontal or Edge Blockiness
Loss of Edges within an Image Block or DC Blockiness
They work as weighting parameters for subjective and objective
measurements with any combination. The results of these different
Artifact Detection Settings
measurement combinations can help to improve picture quality through
the system.
For example, artifact detection can help answer questions such as: “Will
the DMOS be improved with more de-blocking filtering?” or, “Should less
prefiltering be used?”
If edge-blocking weighted DMOS is much greater than blurring-weighted
DMOS, the edge-blocking is the dominant artifact, and perhaps more
de-blocking filtering should be considered.
In some applications, it may be known that added edges, such as ringing
and mosquito noise, are more objectionable than the other artifacts. These
weightings can be customized by the user and configured for the application
to reflect this viewer preference, thus improving DMOS prediction.
Likewise, PSNR can be measured with these artifact weightings to
determine how much of the error contributing to the PSNR measurement
comes from each artifact.
The Attention Model and Artifact Detection can also be used in conjunction
with any combination of picture quality measurements. This allows, for
example, evaluation of how much of a particular noticeable artifact will be
seen where a viewer is most likely to look.
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