&lt;title&gt;Prediction and measurement of high quality in still-image coding&lt;/title&gt;

Avadhanam, Niranjan; Algazi, V. Ralph

doi:10.1117/12.233024

Cited by 5 publications

(1 citation statement)

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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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Perceptual quality metrics applied to still image compression

1998

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Objective picture quality scale (PQS) for image coding

Miyahara

Kotani

Algazi³

1998

IEEE Trans. Commun.

284

173

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| A new methodology for the determination of an objective metric for still image coding is reported. This methodology is applied to obtain a Picture Quality Scale (PQS) for the coding of achromatic images over the full range of image quality de ned by the subjective Mean Opinion Score (MOS). This Picture Quality Scale takes into account the properties of visual perception for b o t hglobal features and localized disturbances. PQS closely approximates the MOS, with a correlation coe cient of more than 0.92, as compared to 0.57 obtained using the conventional WMSE. Extensions and applications of the methodology and of the resulting metric are discussed.Keywords| Image compression, image quality, quality metrics, psychophysics.

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<title>Prediction and measurement of high quality in still-image coding</title>

Cited by 5 publications

References 1 publication

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

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

Perceptual quality metrics applied to still image compression

Objective picture quality scale (PQS) for image coding

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