Image quality metric based on multidimensional contrast perception models

Pons, A.; Malo, Jesús; Artigas, José María Gatell; Capilla, Pascual

doi:10.1016/s0141-9382(99)00009-8

Cited by 32 publications

(33 citation statements)

References 45 publications

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“…Careful analysis shows that both the SSIM index and several recently developed divisive-normalization based masking models exhibit input-dependent behavior in measuring signal distortions [45], [46], [48]. It seems possible that the two approaches may eventually converge to similar solutions.…”

Section: Discussionmentioning

confidence: 99%

“…Again, this may be viewed as an adaptive distortion metric, but unlike previous models, both the size and the shape of the contours are adapted to the underlying signal. Some recent models that use divisive normalization to describe masking effects also exhibit signal-dependent contour orientations (e.g., [45], [46], [48]), although precise alignment with the axes of a polar coordinate system as in Figs. 4(e) and 4(f) is not observed in these methods.…”

Section: B the Structural Similarity (Ssim) Indexmentioning

confidence: 99%

See 1 more Smart Citation

Image Quality Assessment: From Error Visibility to Structural Similarity

Wang

Bovik

Sheikh

et al. 2004

IEEE Trans. on Image Process.

42,069

18,290

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Abstract-Objective methods for assessing perceptual image quality have traditionally attempted to quantify the visibility of errors between a distorted image and a reference image using a variety of known properties of the human visual system. Under the assumption that human visual perception is highly adapted for extracting structural information from a scene, we introduce an alternative framework for quality assessment based on the degradation of structural information. As a specific example of this concept, we develop a Structural Similarity Index and demonstrate its promise through a set of intuitive examples, as well as comparison to both subjective ratings and state-of-the-art objective methods on a database of images compressed with JPEG and JPEG2000. 1

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Section: Discussionmentioning

confidence: 99%

Section: B the Structural Similarity (Ssim) Indexmentioning

confidence: 99%

Image Quality Assessment: From Error Visibility to Structural Similarity

Wang

Bovik

Sheikh

et al. 2004

IEEE Trans. on Image Process.

42,069

18,290

View full text Add to dashboard Cite

show abstract

“…A number of HVS-based methods have been proposed in the literature. Some representative methods include [6]- [13].…”

Section: A Hvs Error-based Qa Methodsmentioning

confidence: 99%

“…While the divisive normalization mechanism for masking effect modeling has been employed by some QA methods [11]- [13], most methods divisively normalize the error signal with visibility thresholds that are dependent on neighborhood signal strength.…”

Section: Similarities With Hvs Based Qa Methodsmentioning

confidence: 99%

An information fidelity criterion for image quality assessment using natural scene statistics

Sheikh

Bovik

Veciana

2005

IEEE Trans. on Image Process.

1,246

615

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Abstract-Measurement of visual quality is of fundamental importance to numerous image and video processing applications. The goal of quality assessment (QA) research is to design algorithms that can automatically assess the quality of images or videos in a perceptually consistent manner. Traditionally, image QA algorithms interpret image quality as fidelity or similarity with a "reference" or "perfect" image in some perceptual space. Such "fullreferenc" QA methods attempt to achieve consistency in quality prediction by modeling salient physiological and psychovisual features of the human visual system (HVS), or by arbitrary signal fidelity criteria. In this paper, we approach the problem of image QA by proposing a novel information fidelity criterion that is based on natural scene statistics. QA systems are invariably involved with judging the visual quality of images and videos that are meant for "human consumption." Researchers have developed sophisticated models to capture the statistics of natural signals, that is, pictures and videos of the visual environment. Using these statistical models in an information-theoretic setting, we derive a novel QA algorithm that provides clear advantages over the traditional approaches. In particular, it is parameterless and outperforms current methods in our testing. We validate the performance of our algorithm with an extensive subjective study involving 779 images. We also show that, although our approach distinctly departs from traditional HVS-based methods, it is functionally similar to them under certain conditions, yet it outperforms them due to improved modeling. The code and the data from the subjective study are available at [1].Index Terms-Image information, image quality assessment (QA), information fidelity, natural scene statistics (NSS).

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“…Examples include measuring mutual information in the wavelet domain [45], contrast perception modeling [46] and modeling the contrast gain control of the HVS [47] However, HVS techniques do not necessarily reflect the true complexity of the visual system and objective measurement of perception remains an open research problem [48][49][50].…”

Section: Objective Quality Assessmentmentioning

confidence: 99%

Objective Quality Assessment in Free-Viewpoint Video Production

Starch

Kilner

Hilton

2008

2008 3DTV Conference: The True Vision - Capture, Transmission and Display of 3D Video

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This paper addresses the problem of objectively quantifying accuracy in free-viewpoint video production. Freeviewpoint video makes use of geometric scene reconstruction and renders novel views using the appearance sampled in multiple camera images. Previous work typically adopts an objective evaluation of geometric accuracy against ground truth data or a subjective evaluation of visual quality in view synthesis. We consider two production scenarios, human performance capture in a highly constrained studio environment and sports production in a large scale external environment. The accuracy of scene reconstruction is typically limited and absolute geometric accuracy does not necessarily reflect the quality of free-viewpoint rendering. A framework is introduced to quantify error at the point of view synthesis. The approach can be applied as a full-reference metric to measure fidelity to a ground truth image or as a no-reference metric to measure the error in rendering. The framework is applied to a data-set with known geometric accuracy and a comparison is presented for studio-based and sports production scenarios.

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Image quality metric based on multidimensional contrast perception models

Cited by 32 publications

References 45 publications

Image Quality Assessment: From Error Visibility to Structural Similarity

Image Quality Assessment: From Error Visibility to Structural Similarity

An information fidelity criterion for image quality assessment using natural scene statistics

Objective Quality Assessment in Free-Viewpoint Video Production

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