Texture Features for Browsing and Retrieval of Image Data
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Model of Saliency-Based Visual Attention for Rapid Scene Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence
Image and Video Quality Assessment Using LCD: Comparisons with CRT Conditions
IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences
Perceptual quality assessment based on visual attention analysis
MM '09 Proceedings of the 17th ACM international conference on Multimedia
Content-partitioned structural similarity index for image quality assessment
Image Communication
Perceptual visual quality metrics: A survey
Journal of Visual Communication and Image Representation
Image quality assessment based on a degradation model
IEEE Transactions on Image Processing
Image quality assessment: from error visibility to structural similarity
IEEE Transactions on Image Processing
An information fidelity criterion for image quality assessment using natural scene statistics
IEEE Transactions on Image Processing
Image information and visual quality
IEEE Transactions on Image Processing
VSNR: A Wavelet-Based Visual Signal-to-Noise Ratio for Natural Images
IEEE Transactions on Image Processing
Visual Attention in Objective Image Quality Assessment: Based on Eye-Tracking Data
IEEE Transactions on Circuits and Systems for Video Technology
Information Content Weighting for Perceptual Image Quality Assessment
IEEE Transactions on Image Processing
FSIM: A Feature Similarity Index for Image Quality Assessment
IEEE Transactions on Image Processing
Image Quality Assessment by Visual Gradient Similarity
IEEE Transactions on Image Processing
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In this paper, a saliency weighted visual feature similarity (SWVFS) metric is proposed for full reference image quality assessment (IQA). Instead of traditional spatial pooling strategies, a visual saliency-based approach is employed for better compliance with properties of the human visual system, where the saliency allocation is closely related to the activity of posterior parietal cortex and the pluvial nuclei of the thalamus. Assuming that the saliency map actually represents the contribution of locally computed visual distortions to the overall image quality, the gradient similarity and the textural congruency are merged into the final image quality indicator. The gradient and texture comparison play complementary roles in characterizing the local image distortion. Extensive experiments conducted on seven publicly available image databases show that the performance of SWVFS is competitive with the state-of-the-art IQA algorithms.