Exploiting entropy masking in perceptual graphic rendering

Dong, Lü; Fang, Yuming; Wang, Lin; Deng, Chenwei; Zhu, Ce; Seah, Hock Soon

doi:10.1016/j.image.2015.02.001

Cited by 7 publications

(4 citation statements)

References 25 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Several approaches with color emphasis is introduced by Albin et al [1], which predict differences in LLAB color space. Dong et al [15] exploit entropy masking, which accounts for the lower sensitivity of the HVS to distortions in unstructured signals, for guiding adaptive rendering of 3D scenes to accelerate rendering.…”

Section: Model-based Perceptual Metricsmentioning

confidence: 99%

A perceptual quality metric for dynamic triangle meshes

Yıldız

Çapın

2017

J Image Video Proc.

View full text Add to dashboard Cite

A measure for assessing the quality of a 3D mesh is necessary in order to determine whether an operation on the mesh, such as watermarking or compression, affects the perceived quality. The studies on this field are limited when compared to the studies for 2D. In this work, we aim a full-reference perceptual quality metric for animated meshes to predict the visibility of local distortions on the mesh surface. The proposed visual quality metric is independent of connectivity and material attributes. Thus, it is not associated to a specific application and can be used for evaluating the effect of an arbitrary mesh processing method. We use a bottom-up approach incorporating both the spatial and temporal sensitivity of the human visual system. In this approach, the mesh sequences go through a pipeline which models the contrast sensitivity and channel decomposition mechanisms of the HVS. As the output of the method, a 3D probability map representing the visibility of distortions is generated. We have validated our method by a formal user experiment and obtained a promising correlation between the user responses and the proposed metric. Finally, we provide a dataset consisting of subjective user evaluation of the quality of public animation datasets.

show abstract

Section: Model-based Perceptual Metricsmentioning

confidence: 99%

A perceptual quality metric for dynamic triangle meshes

Yıldız

Çapın

2017

J Image Video Proc.

View full text Add to dashboard Cite

show abstract

“…If a distortion is visible, the current JND model cannot accurately predict to which extent this distortion affects the visual fidelity of the model. Taking this into consideration requires adding to the model some higher-level properties of the human visual system such as entropy masking [49] or the free energy principle [50]. We think that both of these properties could be properly defined by analyzing the local contrast of the surface in a certain neighborhood.…”

Section: Fmpd Jndmentioning

confidence: 99%

Just Noticeable Distortion Profile for Flat-Shaded 3D Mesh Surfaces

Nader

Wang

Hétroy-Wheeler

et al. 2016

IEEE Trans. Visual. Comput. Graphics

View full text Add to dashboard Cite

It is common that a 3D mesh undergoes some lossy operations (e.g., compression, watermarking and transmission through noisy channels), which can introduce geometric distortions as a change in vertex position. In most cases the end users of 3D meshes are human beings; therefore, it is important to evaluate the visibility of introduced vertex displacement. In this paper we present a model for computing a Just Noticeable Distortion (JND) profile for flat-shaded 3D meshes. The proposed model is based on an experimental study of the properties of the human visual system while observing a flat-shaded 3D mesh surface, in particular the contrast sensitivity function and contrast masking. We first define appropriate local perceptual properties on 3D meshes. We then detail the results of a series of psychophysical experiments where we have measured the threshold needed for a human observer to detect the change in vertex position. These results allow us to compute the JND profile for flat-shaded 3D meshes. The proposed JND model has been evaluated via a subjective experiment, and applied to guide 3D mesh simplification as well as to determine the optimal vertex coordinates quantization level for a 3D model.

show abstract

“…Gabarda et al [39] approximated the probability density function by the spatial and frequency distribution to calculate the pixel-wise entropy on a local basis. The measured variance of the entropy is a function of orientation, which is used as an anisotropic indicator to estimate the fidelity and quality of the image [40]. Although some aggregated features of image grayscale distribution can be embodied in these onedimensional entropy-based methods, the spatial features of the distribution cannot be obtained.…”

Section: Introductionmentioning

confidence: 99%

No-reference color image quality assessment: from entropy to perceptual quality

Chen

Zhang

Lin

et al. 2019

J Image Video Proc.

View full text Add to dashboard Cite

This paper presents a high-performance general-purpose no-reference (NR) image quality assessment (IQA) method based on image entropy. The image features are extracted from two domains. In the spatial domain, the mutual information between the color channels and the two-dimensional entropy are calculated. In the frequency domain, the two-dimensional entropy and the mutual information of the filtered sub-band images are computed as the feature set of the input color image. Then, with all the extracted features, the support vector classifier (SVC) for distortion classification and support vector regression (SVR) are utilized for the quality prediction, to obtain the final quality assessment score. The proposed method, which we call entropy-based no-reference image quality assessment (ENIQA), can assess the quality of different categories of distorted images, and has a low complexity. The proposed ENIQA method was assessed on the LIVE and TID2013 databases and showed a superior performance. The experimental results confirmed that the proposed ENIQA method has a high consistency of objective and subjective assessment on color images, which indicates the good overall performance and generalization ability of ENIQA. The source code is available on github https://github.com/jacob6/ENIQA.

show abstract

Exploiting entropy masking in perceptual graphic rendering

Cited by 7 publications

References 25 publications

A perceptual quality metric for dynamic triangle meshes

A perceptual quality metric for dynamic triangle meshes

Just Noticeable Distortion Profile for Flat-Shaded 3D Mesh Surfaces

No-reference color image quality assessment: from entropy to perceptual quality

Contact Info

Product

Resources

About