Perceptual quality metrics applied to still image compression
Signal Processing - Special issue on image and video quality metrics
A Non-Local Algorithm for Image Denoising
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 2 - Volume 02
Robust NL-means filter with optimal pixel-wise smoothing parameter for statistical image denoising
IEEE Transactions on Signal Processing
Generalizing the Nonlocal-means to super-resolution reconstruction
IEEE Transactions on Image Processing
From Local Kernel to Nonlocal Multiple-Model Image Denoising
International Journal of Computer Vision
A polygon soup representation for multiview coding
Journal of Visual Communication and Image Representation
Perceptual visual quality metrics: A survey
Journal of Visual Communication and Image Representation
Visibility of wavelet quantization noise
IEEE Transactions on Image Processing
Adaptive image coding with perceptual distortion control
IEEE Transactions on Image Processing
Image quality assessment: from error visibility to structural similarity
IEEE Transactions on Image Processing
A perceptually tuned subband image coder based on the measure of just-noticeable-distortion profile
IEEE Transactions on Circuits and Systems for Video Technology
IEEE Transactions on Circuits and Systems for Video Technology
Perceptually-Friendly H.264/AVC Video Coding Based on Foveated Just-Noticeable-Distortion Model
IEEE Transactions on Circuits and Systems for Video Technology
IEEE Transactions on Image Processing
Image quality assessment based on improved structural SIMilarity
PCM'12 Proceedings of the 13th Pacific-Rim conference on Advances in Multimedia Information Processing
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In this paper, we introduce a novel just noticeable difference (JND) threshold estimation model based on a spatial masking function taking both luminance difference and structural regularity into account. Existing spatial masking functions underestimate the JND threshold for irregular textural regions, because they mainly consider the amplitude of luminance change for simplicity. As regular areas show weak masking effect due to their self-similar structures while irregular regions present strong masking effect, the spatial structure directly determines spatial masking. To effectively measure structural regularity in images under different contents, we propose an adaptive non-local self-similarity analysis based procedure. Then we weight luminance differences with similarity coefficients and deduce a new spatial masking function. Finally, an accurate JND estimation model is introduced. Experimental results demonstrate that the proposed JND model has a better visual effect than other models: it injects much noise into the insensitive regions, whereas little into the sensitive regions.