An Image Quality Assessment Algorithm Based on Dual-scale Edge Structure Similarity
ICICIC '07 Proceedings of the Second International Conference on Innovative Computing, Informatio and Control
Range image quality assessment by Structural Similarity
ICASSP '09 Proceedings of the 2009 IEEE International Conference on Acoustics, Speech and Signal 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
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Image quality assessment (IQA) is a crucial technique in perceptual image/video coding, because it is not only a ruler for performance evaluation of coding algorithms but also a metric for ratio-distortion optimization in coding. In this paper, inspired by the fact that distortions of both global and local information influence the perceptual image quality, we propose a novel IQA method that inspects these information in the spatial frequency components of the image. The distortion of the global information mostly existing in low spatial frequency is measured by a rectified mean absolute difference metric, and the distortion of the local information mostly existing in high spatial frequency is measured by SSIM. These two measurements are combined using a newly proposed abruptness weighting that describes the uniformity of the residual image. Experimental results on LIVE database show that the proposed metric outperforms the SSIM and achieves performance competitive with the state-of-the-art metrics.