Texture-based measurement of spatial frequency response using the dead leaves target: extensions, and application to real camera systems

McElvain, Jon S.; Campbell, Scott; Miller, J.; Jin, Elaine W.

doi:10.1117/12.838698

Cited by 33 publications

(33 citation statements)

References 8 publications

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“…One of these is the deviation of the observed signal spectrum from that predicted by the model, as described in Ref. 10. They proposed two extensions to the method aimed at reducing variation (in this case, bias error) from the characteristics assumed by the method.…”

Section: Compute the One-dimensional Mtf Vector By A Radial-average Omentioning

confidence: 99%

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Estimation error in image quality measurements

Burns

2011

SPIE Proceedings

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The development and adoption of standard image quality measurement and analysis methods have helped both the evaluation of competing imaging products and in technologies. Inherent in the interpretation of results from any particular evaluation, however, are the variation of the method itself, the sampling of test images, equipment, and test conditions. Here we take a statistical approach to measurement variation, and interpret the objective as being the estimation of particular system or image properties, based on data, collected as part of standard testing. Measurement variation was investigated for two signal-transfer methods commonly used for digital camera and scanner evaluation: the ISO 12233 slanted-edge spatial frequency response and the dead-leaves method for texture-MTF evaluation being developed by the Camera Phone Image Quality (CPIQ) Initiative. In each case, the variation due to the selection of analysis regions was computed by repeated analysis. The slanted-edge methods indicated a relative error in the range of 1-3% depending on the nature of the region selection. For the dead-leaves method, the amplitude spectrum (square root of the noise-power spectrum) showed a relative error of approximately 4-6%, however, this can be reduced by applying spectral estimation methods commonly used in image noise analysis.

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Section: Compute the One-dimensional Mtf Vector By A Radial-average Omentioning

confidence: 99%

“…Less information was given for the particulars of the sampling and estimation to be used. McElvain et al 10 refer to a second method for estimating the effective "texture MTF" by reversing steps 4 and 5.…”

Section: Compute the One-dimensional Mtf Vector By A Radial-average Omentioning

confidence: 99%

Estimation error in image quality measurements

Burns

2011

SPIE Proceedings

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“…They do so by replicating statistical properties of natural scenes to trigger non-linear content-aware processes at 'normal' levels [4]. The direct dead leaves implementation [6] (Equation 2) improves accuracy by subtracting the system's NPS measured from a uniform patch ( 234534 ( )), from the output target power spectrum ( 634534 ); 78534 is the input target power spectrum; is the spatial frequency. A further intrinsic implementation characterizes performance of the lens and imager; it is not sensitive to reversible image processing such as sharpening or contrast stretching [7], which affect perceived image quality.…”

Section: Modulation Transfer Function (Mtf)mentioning

confidence: 99%

“…The Camera Phone Image Quality (CPIQ) group's MF-IQM [5] models the combined effect of color saturation, color uniformity, geometric lens distortion, chromatic aberration, edge-sharpness, texture-sharpness, and visual noise. Texture-sharpness and edgesharpness metrics employed are univariate acutance STV-IQMs, implementing direct dead leaves [6] and ISO 12233 slanted-edge [2] MTFs respectively. The visual noise metric follows Annex B of ISO 15739 [16] and uses uniform patch noise measurements.…”

Section: Multivariate Formalism (Mf-iqm)mentioning

confidence: 99%

Bridging the Gap Between Imaging Performance and Image Quality Measures

Fry¹,

Triantaphillidou²,

Jacobson³

et al. 2018

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“…This form of local decision-making reduces the effectiveness of the Dead Leaves texture evaluation method, in particular the separation of image (signal) texture fluctuations from noise, estimated from uniform areas. [5] Other methods for texture capture evaluation are also influenced by these noise-reduction methods. A radial sine-wave target, also called a Siemens Star, is used to evaluate an effective signal MTF.…”

Section: Introductionmentioning

confidence: 99%

Embedded signal approach to image texture reproduction analysis

Burns¹,

Baxter²

2014

SPIE Proceedings

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Since image processing aimed at reducing image noise can also remove important texture, standard methods for evaluating the capture and retention of image texture are currently being developed. Concurrently, the evolution of the intelligence and performance of camera noise-reduction (NR) algorithms poses a challenge for these protocols. Many NR algorithms are 'content-aware', which can lead to different levels of NR being applied to various regions within the same digital image. We review the requirements for improved texture measurement. The challenge is to evaluate image signal (texture) content without having a test signal interfere with the processing of the natural scene. We describe an approach to texture reproduction analysis that uses embedded periodic test signals within image texture regions. We describe a target that uses natural image texture combined with a multi-frequency periodic signal. This low-amplitude signal region is embedded in the texture image. Two approaches for embedding periodic test signals in image texture are described. The stacked sine-wave method uses a single combined, or stacked, region with several frequencycomponents. The second method uses a low-amplitude version of the IEC-61146-1 sine-wave multi-burst chart, combined with image texture. A 3x3 grid of smaller regions, each with a single frequency, constitutes the test target. Both methods were evaluated using a simulated digital camera capture-path that included detector noise and optical MTF, for a range of camera exposure/ISO settings. Two types of image texture were used with the method, natural grass and a computed 'dead-leaves' region composed of random circles. The embedded-signal methods tested for accuracy with respect to image noise over a wide range of levels, and then further in an evaluation of an adaptive noise-reduction image processing.

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Texture-based measurement of spatial frequency response using the dead leaves target: extensions, and application to real camera systems

Cited by 33 publications

References 8 publications

Estimation error in image quality measurements

Estimation error in image quality measurements

Bridging the Gap Between Imaging Performance and Image Quality Measures

Embedded signal approach to image texture reproduction analysis

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