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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