The visible differences predictor: an algorithm for the assessment of image fidelity
Digital images and human vision
What's wrong with mean-squared error?
Digital images and human vision
Perceptual quality metrics applied to still image compression
Signal Processing - Special issue on image and video quality metrics
Coherence-Enhancing Diffusion Filtering
International Journal of Computer Vision
Image quality assessment based on multiscale geometric analysis
IEEE Transactions on Image Processing
Image and Vision Computing
Image quality assessment based on perceptual structural similarity
PReMI'07 Proceedings of the 2nd international conference on Pattern recognition and machine intelligence
Study of subjective and objective quality assessment of video
IEEE Transactions on Image Processing
Content-partitioned structural similarity index for image quality assessment
Image Communication
Perceptual visual quality metrics: A survey
Journal of Visual Communication and Image Representation
A filter bank for the directional decomposition of images: theoryand design
IEEE Transactions on Signal Processing
Structural information-based image quality assessment using LU factorization
IEEE Transactions on Consumer Electronics
Image quality assessment: from error visibility to structural similarity
IEEE Transactions on Image Processing
The contourlet transform: an efficient directional multiresolution image representation
IEEE Transactions on Image Processing
A distortion measure for blocking artifacts in images based on human visual sensitivity
IEEE Transactions on Image Processing
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We propose an improved objective image quality assessment method based on the structural similarity and visual masking, called the Perceptual Image Quality Assessment (PIQA). The PIQA contains three similarity measures: the luminance comparison measure, the structure comparison measure, the contrast comparison measure as same as the Structure Similarity (SSIM) and its variants. Firstly, in order to improve the ability of distinguishing the structure information in blurred images and noisy images, we modify the structure comparison measure by using the improved structure tensor which is more efficient for describing the structure information in global areas. Secondly, based on the perceptual characters of Human Visual System (HVS) perceptual process, the contrast masking and neighborhood masking are integrated to the contrast comparison measure. Finally, three measures are pooled together to compute the PIQA metric. Comparing with the state-of-the-art methods including Multi-scale SSIM (MS-SSIM), Visual Signal to Noise Ratio (VSNR) and Visual Information Fidelity (VIF) criterion, simulation results show that our approach is highly consistent with HVS perceptual process, and also delivers better performance.