Perceptual color noise formulation

Kuang, Jiangtao; Jiang, Xiaoyun; Quan, Shuxue; Chiu, Andrew

doi:10.1117/12.587807

Cited by 7 publications

(9 citation statements)

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“…The study of Kuang et al 1 is perhaps the closest in the literature to meeting the above criteria, and so the present work will be contrasted with it. Kuang et al created their own neutral noise ruler and calibrated it in terms of just noticeable differences (JNDs) of noisiness through forced-choice paired comparison.…”

Section: Introductionmentioning

confidence: 91%

“…Therefore, different color spaces and weights can be compared for predictiveness. It is well known that the perception of noise is dominated by the achromatic (luminance) component 9 , though there are chromatic noise contributions as well 1,12 , as reflected in the noise definition for computing noise-based ISO speed in digital still cameras 13 . What is less well known is that the change of perceived noisiness as a function of tonal value, at constant objective noise, is closely predicted 14 by the nonlinear transform from luminance to CIE lightness (L*), despite the fact that L* was defined based on perception of brightness of uniform fields, not visibility of rapidly fluctuating noise.…”

Section: Objective Metric and Quality Loss Functionmentioning

confidence: 99%

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Development of a perceptually calibrated objective metric of noise

Keelan¹,

Jin²,

Prokushkin³

2011

SPIE Proceedings

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A system simulation model was used to create scene-dependent noise masks that reflect current performance of mobile phone cameras. Stimuli with different overall magnitudes of noise and with varying mixtures of red, green, blue, and luminance noises were included in the study. Eleven treatments in each of ten pictorial scenes were evaluated by twenty observers using the softcopy ruler method. In addition to determining the quality loss function in just noticeable differences (JNDs) for the average observer and scene, transformations for different combinations of observer sensitivity and scene susceptibility were derived. The psychophysical results were used to optimize an objective metric of isotropic noise based on system noise power spectra (NPS), which were integrated over a visual frequency weighting function to yield perceptually relevant variances and covariances in CIE L*a*b* space. Because the frequency weighting function is expressed in terms of cycles per degree at the retina, it accounts for display pixel size and viewing distance effects, so application-specific predictions can be made. Excellent results were obtained using only L* and a* variances and L*a* covariance, with relative weights of 100, 5, and 12, respectively. The positive a* weight suggests that the luminance (photopic) weighting is slightly narrow on the long wavelength side for predicting perceived noisiness. The L*a* covariance term, which is normally negative, reflects masking between L* and a* noise, as confirmed in informal evaluations. Test targets in linear sRGB and rendered L*a*b* spaces for each treatment are available at http://www.aptina.com/ImArch/ to enable other researchers to test metrics of their own design and calibrate them to JNDs of quality loss without performing additional observer experiments. Such JND-calibrated noise metrics are particularly valuable for comparing the impact of noise and other attributes, and for computing overall image quality.

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Section: Introductionmentioning

confidence: 91%

Section: Objective Metric and Quality Loss Functionmentioning

confidence: 99%

Development of a perceptually calibrated objective metric of noise

Keelan¹,

Jin²,

Prokushkin³

2011

SPIE Proceedings

View full text Add to dashboard Cite

show abstract

“…The optimal power number is determined through several trials by choosing different combinations of coefficients in Equation (4). For the specific camera that was used in the study, the optimal power gamma is determined to be 1/1.8191 k = .…”

Section: Determination Of Gamma Curve For Digital Cameramentioning

confidence: 99%

“…To balance noise performance versus color fidelity and local contrast preservation, in the complete framework, a noise metric is formulated in CIELAB color space [4] : The total cost function between the original image and transformed image is described as follows:…”

Section: Algorithmmentioning

confidence: 99%

Nonlinear curve optimization in digital imaging

Quan

Jiang

2007

SPIE Proceedings

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High dynamic compression of natural scenes and nonlinearity compensation of output devices are demanded as the output devices have limited dynamic range and color gamut, and devices such as Standard-RGB compliant displays and printers have different nonlinear responses to linear inputs. This paper describes a general framework of nonlinear curve optimization in digital imaging and its application to the determination of gamma look up table through curve parameterization. Three factors are considered, that is, the color appearance fidelity, the feature preservation, and the suppression of noise propagation.

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“…luminance and chrominance components of the image [23]. ISO 15739 proposes a visual noise metric that performs contrast sensitivity-weighted integration of the measured NPS over spatial frequency,…”

Section: Wee124mentioning

confidence: 99%

Image Quality Standards in Automotive Vision Applications

Hertel¹,

Chang²

2007

2007 IEEE Intelligent Vehicles Symposium

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Digital cameras are increasingly used in automotive applications. As these cameras are integrated into active safety systems, image quality becomes ever more important. The captured image information is limited not only by the sensitivity and signal-to-noise ratio of the image sensor, but also by such image capture conditions as ambient lighting, camera-object distance, and relative camera-object velocity. For example, the detection of a pedestrian at night might be further impeded by the headlights of oncoming traffic. Image quality performance characterizes the ability of the imaging system to capture vital image information under application-typical capture conditions. Characterizing objective image quality performance requires clearly defined image quality attributes and metrics. This paper introduces the basic concepts of objective and subjective image quality, reviews existing image quality metrics and standards that have been developed for digital still and video applications, and explores their applicability for automotive uses where image information is interpreted by a human observer or machine vision application. The automotive photospace is introduced as a useful tool to characterize the automotive image capture conditions, and distinguish them from other still and video applications. As automotive imaging becomes more widespread, early standardization of image quality is important. This will enable automotive camera suppliers and the automotive industry to communicate in a common language when specifying imaging systems so that sufficient image quality under application conditions is ensured.

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Perceptual color noise formulation

Cited by 7 publications

References 0 publications

Development of a perceptually calibrated objective metric of noise

Development of a perceptually calibrated objective metric of noise

Nonlinear curve optimization in digital imaging

Image Quality Standards in Automotive Vision Applications

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