A Fidelity Metric for Assessing Visual Quality of Color Images

Chou, Chun-Hsien; Liu, Kuo-Cheng

doi:10.1109/icccn.2007.4317975

Cited by 24 publications

(19 citation statements)

References 18 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…These metrics have been selected from the wide variety of mono-channels metrics proposed in the literature based on two criteria: The approach for choosing the multi-channel metrics is quite different, since in the state of the art, when considering the multi-channel case, few algorithms have been proposed, for example those proposed in [14] [15] [16]. In fact, the management of the color information when evaluating the quality of natural scenes is still a very open research challenge.…”

Section: Selected Full Reference Metrics For Objective Quality Assessmentioning

confidence: 99%

A comparative study of color image compression standards using perceptually driven quality metrics

Simone

Ticca

Dufaux

et al. 2008

Applications of Digital Image Processing XXXI

View full text Add to dashboard Cite

The task of comparing the performance of different codecs is strictly related to the research in the field of objective quality metrics. Even if several objective quality metrics have been proposed in literature, the lack of standardization in the field of objective quality assessment and the lack of extensive and reliable comparisons of the performance of the different state-of-the-art metrics often make the results obtained using objective metrics not very reliable. In this paper we aim at comparing the performance of three of the existing alternatives for compression of digital pictures, i.e. JPEG, JPEG 2000, and JPEG XR compression, by using different objective Full Reference metrics and considering also perceptual quality metrics which take into account the color information of the data under analysis.

show abstract

Section: Selected Full Reference Metrics For Objective Quality Assessmentioning

confidence: 99%

A comparative study of color image compression standards using perceptually driven quality metrics

Simone

Ticca

Dufaux

et al. 2008

Applications of Digital Image Processing XXXI

View full text Add to dashboard Cite

show abstract

“…If no more information is available, it is recommended to set these parameters to one [46]. However, some studies (e.g., [49,50]) have shown that the performance of the CIEDE2000 formula in estimating perceptual color differences can be improved by appropriately setting those parameters. These studies have also found that the background luminance and the amount of noise are important factors to determine the appropriate value of the CIEDE2000 parameters.…”

Section: Parameters Of the Ciede2000 Formulamentioning

confidence: 99%

“…Based on the formulation given in [50], the following equations for the CIEDE2000 parameters are proposed:…”

Section: Parameters Of the Ciede2000 Formulamentioning

confidence: 99%

“…Liu [50] have found that the effect of the background on the estimation of color differences is approximately a linear function of the mean background luminance Y B , which is the second color component of the mean background color expressed in the XYZ color model. Additionally, only parameters k C and k H are affected by background luminance, since the latter only affects the chromatic channels.…”

Section: Parameters Of the Ciede2000 Formulamentioning

confidence: 99%

See 1 more Smart Citation

Edge-preserving color image denoising through tensor voting

Moreno

García

Puig

et al. 2011

Computer Vision and Image Understanding

View full text Add to dashboard Cite

This paper presents a new method for edge-preserving color image denoising based on the tensor voting framework, a robust perceptual grouping technique used to extract salient information from noisy data. The tensor voting framework is adapted to encode color information through tensors in order to propagate them in a neighborhood by using a specific voting process. This voting process is specifically designed for edge-preserving color image denoising by taking into account perceptual color differences, region uniformity and edginess according to a set of intuitive perceptual criteria. Perceptual color differences are estimated by means of an optimized version of the CIEDE2000 formula, while uniformity and edginess are estimated by means of saliency maps obtained from the tensor voting process. Measurements of removed noise, edge preservation and undesirable introduced artifacts, additionally to visual inspection, show that the proposed method has a better performance than the state-of-the-art image denoising algorithms for images contaminated with CCD camera noise.

show abstract

“…In contrast, noise is often used for half-toning to improve the smoothness of color tone continuity [1,2], and for improving image quality [3,4]. In any cases, exploiting the human visual system (HVS) is important to increase the quality of an image or to measure image quality [5,6,7].…”

Section: Introductionmentioning

confidence: 99%

The dependence of visual noise perception on background color and luminance

Shohara

Kotani

2010

28th Picture Coding Symposium

View full text Add to dashboard Cite

This paper describes the dependency of noise perception on background color and luminance of noise quantitatively. We conduct subjective and quantitative experiments for three noise models, using a modified grayscale method. The subjective experiment results show the perceived color noise depends on the background color, but the perceived luminance noise does not. The most sensitive background colors for color noises are yellow and purple. The perceived noises against background gray level show the similar trend between noise models. When the background gray level is L*~25, we perceive the noise best. In addition, the perceived chromatic noise level is about 8 times smaller than the calculated color noise using CIELAB Euclidean distance.

show abstract

A Fidelity Metric for Assessing Visual Quality of Color Images

Cited by 24 publications

References 18 publications

A comparative study of color image compression standards using perceptually driven quality metrics

A comparative study of color image compression standards using perceptually driven quality metrics

Edge-preserving color image denoising through tensor voting

The dependence of visual noise perception on background color and luminance

Contact Info

Product

Resources

About