Objective

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Neuroscientists have studied the Bayesian brain perception theory

which indicates that the human vision system indirectly processes input signals during the processing of input images. A complete set of intrinsic derivation mechanisms actively predicts and understands input image information and attempts to ignore any uncertainty information in an image. In other words

given an input image

the brain does not fully process the input visual information

but it has an intrinsic derivation mechanism that enables it to actively predict the gross structure of the image

including certain information (structured information). At the same time

uncertain information (unstructured information)

such as residual clutter

is ignored to realize the understanding and perception of the image. In considering the role of structured information in just noticeable distortion (JND) estimation on natural images

a sparse representation-based structured/unstructured information separation model is proposed and applied to the JND threshold estimation. The proposed method achieves great consistency with the human visual system in terms of the perceived JND threshold.

Method

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Initially

90 natural images are selected for dictionary learning. These training images are pre-processed

and each image is divided into 8×8 non-overlapping image blocks. The variance of each image block is calculated

and the image blocks with high variances are selected as training samples. Then

an over-complete dictionary is learned from a set of training samples using the classical K-singular value decomposition algorithm. Then

the input natural image is reconstructed by sparse representation using the previously learned dictionary via the orthogonal matching pursuit (OMP) algorithm. The corresponding structural layer and non-structural layer of the input natural image can be obtained by setting an appropriate iteration number during the implementation of the OMP algorithm. Subsequently

we further design different JND estimation models for structural and non-structural layers. 1) Luminance adaptability and contrast mask-based JND estimation model for structural layers. The JND threshold value of an image is mainly related to the brightness adaptability of the visual system

contrast mask

module mask

and image structure. Thus

the luminance adaptability function and contrast mask equation are derived under the experimental environment of a regular structure. The JND calculation model of the structural layer is derived from the fusion of the two models. 2) Luminance contrast and information uncertainty-based JND estimation model for non-structural layer. The modular mask effect reveals the visibility of stimuli in the visual system because of the interaction or interference among visual stimuli in the visual content of the input scene. When the structure of the visual content is ordered and the background is uniform

the module mask effect is extremely weak

and the spatial object is easily detected. On the contrary

when the visual content is disordered and uncertain

the module mask effect is enhanced

that is

the detection of space objects is suppressed. Therefore

the module mask effect is related not only to brightness contrast but also to information uncertainty. Therefore

we construct an unstructured layer of the JND model on the basis of the module mask combined with information uncertainty and brightness contrast. Finally

given the overlap between the structural layer of JND and the non-structural layer of JND

using a simple linear sum to fuse the two layers is impossible

and the overlapping parts must be removed. A nonlinear additive model describing the masking effect between different components is utilized to fuse the two JND estimation results.

Result

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Three existing JND models are selected for comparison. For a fair comparison

the same noise is injected into the original image through the JND models

and then the visual effects of the polluted image are compared. The subjective experimental results show that the proposed JND model can better guide the distribution of noise and avoid the sensitive region of human vision relative to other JND models when the same noise is injected. The proposed JND model is also consistent with the subjective visual perception of human eyes. To further verify the fairness

we compare the four JND models using the classical peak signal-to-noise ratio (PSNR). The PSNRs of the contaminated Goddess image and contaminated Lena images are compared. The objective experimental results show that the PSNRs of the proposed model are significantly higher than those of the other three JND models. The proposed JND estimation model uses sparse representation to separate the structured and unstructured information of the input natural image. It then calculates the JND threshold according to the characteristics of different components. The process is consistent with the mechanism of human visual perception. Therefore

the proposed JND estimation model can effectively and accurately predict the JND threshold of natural images.

Conclusion

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Compared with the existing relevant models

the proposed JND model can effectively predict the JND threshold of natural images

and it is much more consistent with human visual perception.