Quality image metrics for synthetic images based on perceptual color differences

Albin, Stéphane; Rougeron, Gilles; Péroche, Bernard; Trémeau, Alain

doi:10.1109/tip.2002.802544

Cited by 14 publications

(10 citation statements)

References 36 publications

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“…For reasons that will be presented later, it seems acceptable to consider the possibility of using the following three types of evaluation methods: -Methods involving human observers: who rate sets of visualizations, allowing the computation of quality measures, as it the case in image quality evaluation 51,52 , or ROC studies 53,54 ; -Quality indices: widely used in image quality evaluation [55][56][57][58][59] , can be obtained directly from some kind of measure that seem relevant to the quality of the visualization, computed directly from the application of the visualization technique to the data 29 ; -Digital observers: that could use models of the Human Visual System (HVS), such as the ones described in [60][61][62] to estimate ratings that human observers would attribute to visualizations.…”

Section: Evaluation Methodsmentioning

confidence: 99%

Quality evaluation in medical visualization: some issues and a taxonomy of methods

Santos

Dillenseger²

2005

SPIE Proceedings

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Among the several medical imaging stages (acquisition, reconstruction, etc.), visualization is the latest stage on which decision is generally taken. Scientific visualization tools allow to process complex data into a graphical visible and understandable form, the goal being to provide new insight. If the evaluation of procedures is a crucial issue and a main concern in medicine, paradoxically visualization techniques, predominantly in tri-dimensional imaging, have not been the subject of many evaluation studies. This is perhaps due to the fact that the visualization process integrates the Human Visual and Cognitive Systems, which makes evaluation especially difficult. However, as in medical imaging, the question of quality evaluation of a specific visualization remains a main challenge. While a few studies concerning specific cases have already been published, there is still a great need for definition and systemization of evaluation methodologies. The goal of our study is to propose such a framework, which makes it possible to take into account all the parameters taking part in the evaluation of a visualization technique. Concerning the problem of quality evaluation in data visualization in general, and in medical data visualization in particular, three different concepts appear to be fundamental: the type and level of components used to convey to the user the information contained in the data, the type and level at which evaluation can be performed, and the methodologies used to perform such evaluation. We propose a taxonomy involving types of methods that can be used to perform evaluation at different levels.

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Section: Evaluation Methodsmentioning

confidence: 99%

Quality evaluation in medical visualization: some issues and a taxonomy of methods

Santos

Dillenseger²

2005

SPIE Proceedings

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“…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%

“…For example, network based applications require 3D model compression and streaming, in which a tradeoff must be made between the visual quality and the transmission speed. Several applications require level-of-detail (LOD) simplification of 3D meshes *Correspondence: zeynep.cipiloglu@cbu.edu.tr 1 Faculty of Engineering, Celal Bayar University, Muradiye/Manisa, Turkey Full list of author information is available at the end of the article for fast processing and rendering optimization. Watermarking of 3D meshes requires evaluation of quality due to artifacts produced.…”

Section: Introductionmentioning

confidence: 99%

A perceptual quality metric for dynamic triangle meshes

Yıldız

Çapın

2017

J Image Video Proc.

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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.

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“…Three image-quality metrics based on perceptual color differences are proposed by Albin et al [34]. These similar metrics find the difference between two images in the LLAB (a modified version of CIELAB) color space.…”

Section: B Perceptually Based Metricsmentioning

confidence: 99%

Assessing Visual Quality of 3-D Polygonal Models

Bulbul

Çapın

Lavoué

et al. 2011

IEEE Signal Process. Mag.

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Abstract-This survey paper presents recent advances in evaluating and measuring the perceived visual quality of 3D polygonal models. The paper analyzes the general process of objective quality assessment metrics and subjective user evaluation methods and presents a taxonomy of existing solutions. Simple geometric error computed directly on the 3D models does not necessarily reflect the perceived visual quality; therefore, integrating perceptual issues for 3D quality assessment is of great significance. This paper discusses existing metrics, including perceptually based ones, computed either on 3D data or on 2D projections, and evaluates their performance for their correlation with existing subjective studies.

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Quality image metrics for synthetic images based on perceptual color differences

Cited by 14 publications

References 36 publications

Quality evaluation in medical visualization: some issues and a taxonomy of methods

Quality evaluation in medical visualization: some issues and a taxonomy of methods

A perceptual quality metric for dynamic triangle meshes

Assessing Visual Quality of 3-D Polygonal Models

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