What's wrong with mean-squared error?
Digital images and human vision
BT Technology Journal
A single-ended blockiness measure for JPEG-Coded images
Signal Processing
Wireless Communications
Digital Video Image Quality and Perceptual Coding (Signal Processing and Communications)
Digital Video Image Quality and Perceptual Coding (Signal Processing and Communications)
Image quality assessment: from error visibility to structural similarity
IEEE Transactions on Image Processing
Image information and visual quality
IEEE Transactions on Image Processing
IEEE Transactions on Image Processing
VSNR: A Wavelet-Based Visual Signal-to-Noise Ratio for Natural Images
IEEE Transactions on Image Processing
Image quality assessment based on the contourlet transform
CAR'10 Proceedings of the 2nd international Asia conference on Informatics in control, automation and robotics - Volume 1
Full-Reference Image Quality Metrics: Classification and Evaluation
Foundations and Trends® in Computer Graphics and Vision
Human observer confidence in image quality assessment
Image Communication
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part IV
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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 user-oriented 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 (NHIQM) and a perceptual relevance weighted L"p-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, NHIQM and L"p-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 NHIQM performs the pooling at the transmitter of the system to produce a single value as reduced-reference, the L"p-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, non-linear 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 L"p-norm reveals their excellent correlation with human perception in terms of accuracy, monotonicity, and consistency. This holds not only for the prediction performance on images taken for the training of the metrics but also for the generalization to unknown images. In addition, it is shown that the NHIQM approach and the perceptual relevance weighted L"p-norm outperform other prominent objective quality metrics in prediction performance.