10,838 research outputs found
Reduced-reference metric design for objective perceptual quality assessment in wireless imaging
The rapid growth of third and development of future generation mobile systems has led
to an increase in the demand for image and video services. However, the hostile nature
of the wireless channel makes the deployment of such services much more challenging,
as in the case of a wireline system. In this context, the importance of taking care of user
satisfaction with service provisioning as a whole has been recognized. The related useroriented
quality concepts cover end-to-end quality of service and subjective factors such
as experiences with the service. To monitor quality and adapt system resources,
performance indicators that represent service integrity have to be selected and related
to objective measures that correlate well with the quality as perceived by humans. Such
objective perceptual quality metrics can then be utilized to optimize quality perception
associated with applications in technical systems.
In this paper, we focus on the design of reduced-reference objective perceptual
image quality metrics for use in wireless imaging. Specifically, the normalized hybrid
image quality metric (NHlQM) and a perceptual relevance weighted Lp-norm are
designed. The main idea behind both feature-based metrics relates to the fact that the
human visual system (HVS)is trained to extract structural information from the viewing
area. Accordingly, NHlQMand Lp-norm are designed to account for different structural
artifacts that have been observed in our distortion model of a wireless link. The extent
by which individual artifacts are present in a given image is obtained by measuring
related image features. The overall quality measure is then computed as a weighting
sum of the features with the respective perceptual relevance weight obtained from
subjective experiments. The proposed metrics differ mainly in the pooling of the
features and amount of reduced-reference produced. While NHlQM performs the
pooling at the transmitter of the system to produce a single value as reduced-reference,
the Lp-norm requires all involved feature values from the transmitted and received
image to perform the pooling on the feature differences at the receiver. In addition, nonlinear
mapping functions are developed that relate the metric values to predicted mean
opinion scores (MOS) and account for saturations in the HVS. The evaluation of
prediction performance of NHIQM and the Lp-norm reveals their excellent correlation
with human perception in terms of accuracy, monotonicity, and consistency
- …