Screen content image quality assessment using distortion-based directional edge and gradient similarity maps

Tolie, Hamidreza Farhadi; Faraji, Mohammad Reza

doi:10.1016/j.image.2021.116562

Cited by 4 publications

(1 citation statement)

References 33 publications

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“…Therefore, designing an effective NR SCIs quality assessment method is necessary and meaningful. At present, SCIs quality assessment methods also can be grouped into traditional methods [11][12][13][14][15][16][17] and CNN based methods [18,19]. Compared with the traditional method, the CNN based method can automatically extract representative features hidden in image.…”

Section: Introductionmentioning

confidence: 99%

Quality Assessment of Screen Content Images Based on Convolutional Neural Network Considering Binocular Visual Mechanism

Chang

Liu

et al. 2022

Preprint

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In this paper, a dual-pathway convolutional neural network (CNN) considering binocular visual mechanism is proposed to simulate the perceiving characteristics from binocular vision. Because the different sensitivity of retinal photoreceptor cells to RGB colors and the characteristics of human visual attention are more important in binocular vision mechanism, an attention module is used to weight the RGB channels and theirs patial position on each channel. Meanwhile, considering that 3D convolution can learn inter-frame information well, we use 3D convolution to extract the correlation features between RGB channels. Furthermore,considering the importance of optic chiasm in human visual system (HVS), a new cross-method is design to simulate the optic chiasm. Besides, since the characteristics of multi-scale and multi-level are indispensable to perception of any objects, a new multi-scale and multi-level feature fusion (MSMLFF) module is constructed to extract the perceptual features at different scales andlevels. The experimental results illustrate that the proposed method outperforms several state-of-the-art SCIs quality assessment methods.

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