Spatiotemporal just noticeable difference modeling with heterogeneous temporal visual features

Xing, Yafen; Yin, Haibing; Zhou, Yang; Chen, Yong; Yan, Chenggang

doi:10.1016/j.displa.2021.102096

Cited by 5 publications

(9 citation statements)

References 55 publications

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“…According to [ 44 ], information content can be measured by the prior probability distribution and the likelihood function. It has been demonstrated that these measurements are consistent across human subjects and can be modeled using simple parametric functions [ 42 , 45 ]. Motivated by this, this paper adopts the probability distribution function and fitting curve to measure color information content induced by color feature parameters.…”

Section: Analysis Of Color Feature Parametersmentioning

confidence: 99%

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Pixel-Domain Just Noticeable Difference Modeling with Heterogeneous Color Features

Yin

Wang

et al. 2023

Sensors

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With the rapidly emerging user-generated images, perception compression for color image is an inevitable mission. Whilst in existing just noticeable difference (JND) models, color-oriented features are not fully taken into account for coinciding with HVS perception characteristics, such as sensitivity, attention, and masking. To fully imitate the color perception process, we extract color-related feature parameters as local features, including color edge intensity and color complexity, as well as region-wise features, including color area proportion, color distribution position and color distribution dispersion, and inherent feature irrelevant to color content called color perception difference. Then, the potential interaction among them is analyzed and modeled as color contrast intensity. To utilize them, color uncertainty and color saliency are envisaged to emanate from feature integration in the information communication framework. Finally, color and uncertainty saliency models are applied to improve the conventional JND model, taking the masking and attention effect into consideration. Subjective and objective experiments validate the effectiveness of the proposed model, delivering superior noise concealment capacity compared with start-of-the-art works.

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Section: Analysis Of Color Feature Parametersmentioning

confidence: 99%

“…For example, compared with the pure-color background, some color sharp-change areas in the images are perceptually more noisy. That is to say, color complexity prevents the visual system from acquiring accurate information, which can be equivalent to the perceptual noise in the visual channel of HVS, as in [ 45 ].…”

Section: The Proposed Jnd Modelmentioning

confidence: 99%

Pixel-Domain Just Noticeable Difference Modeling with Heterogeneous Color Features

Yin

Wang

et al. 2023

Sensors

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“…An overview of these factors can be found in the survey by researchers in [ 3 ]. Using the computational domain as a classification criterion, there are two branches of existing JND models, i.e., the pixel domain (where JND thresholds are computed directly for each pixel) [ 4 , 5 , 6 , 7 , 8 , 9 , 10 , 11 , 12 , 13 ] and the transform domain (where the image is first transformed into a subband domain, and then JND thresholds are calculated for each subband) [ 14 , 15 , 16 , 17 , 18 , 19 , 20 , 21 , 22 , 23 , 24 ]. Nevertheless, both model types generally follow the same design philosophy of simulating the visual-masking effects of several elements before combining (multiplying or adding) them to obtain an overall JND estimation.…”

Section: Introductionmentioning

confidence: 99%

“…Subsequently, a more realistic LA function was proposed to improve JND estimation by integrating a block classification (plain, edge, and texture) strategy [ 16 ]. For video, corresponding video JND models for the DCT domain were proposed considering spatiotemporal CSF and eye movement compensation [ 17 ], LA effect based on gamma correction and CM effect based on more accurate block classification [ 18 ], texture complexity and frequency, visual sensitivity and visual attention [ 19 ], the various sizes of DCT blocks (from 4 × 4 to 32 × 32) [ 20 ], motion direction [ 21 ], fovea masking [ 22 ], temporal duration and residual fluctuations [ 23 ].…”

Section: Introductionmentioning

confidence: 99%

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Semantically Adaptive JND Modeling with Object-Wise Feature Characterization, Context Inhibition and Cross-Object Interaction

Wang

Yin

et al. 2023

Sensors

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Performance bottlenecks in the optimization of JND modeling based on low-level manual visual feature metrics have emerged. High-level semantics bear a considerable impact on perceptual attention and subjective video quality, yet most existing JND models do not adequately account for this impact. This indicates that there is still much room and potential for performance optimization in semantic feature-based JND models. To address this status quo, this paper investigates the response of visual attention induced by heterogeneous semantic features with an eye on three aspects, i.e., object, context, and cross-object, to further improve the efficiency of JND models. On the object side, this paper first focuses on the main semantic features that affect visual attention, including semantic sensitivity, objective area and shape, and central bias. Following that, the coupling role of heterogeneous visual features with HVS perceptual properties are analyzed and quantified. Second, based on the reciprocity of objects and contexts, the contextual complexity is measured to gauge the inhibitory effect of contexts on visual attention. Third, cross-object interactions are dissected using the principle of bias competition, and a semantic attention model is constructed in conjunction with a model of attentional competition. Finally, to build an improved transform domain JND model, a weighting factor is used by fusing the semantic attention model with the basic spatial attention model. Extensive simulation results validate that the proposed JND profile is highly consistent with HVS and highly competitive among state-of-the-art models.

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Audio–video collaborative JND estimation model for multimedia applications

Sheng,

Yin,

Wang

et al. 2024

Journal of Visual Communication and Image Representation

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Spatiotemporal just noticeable difference modeling with heterogeneous temporal visual features

Cited by 5 publications

References 55 publications

Pixel-Domain Just Noticeable Difference Modeling with Heterogeneous Color Features

Pixel-Domain Just Noticeable Difference Modeling with Heterogeneous Color Features

Semantically Adaptive JND Modeling with Object-Wise Feature Characterization, Context Inhibition and Cross-Object Interaction

Audio–video collaborative JND estimation model for multimedia applications

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