Towards the Development of the IEEE P1858 CPIQ Standard &amp;#x2013; A validation study

Tseng, B.; Chang, Ed; Jin, Elaine W.; Phillips, Jonathan; Belska, M; Farnand, Susan; Tran, Vinh; Wang, Yixuan

doi:10.2352/issn.2470-1173.2017.12.iqsp-249

Cited by 13 publications

(17 citation statements)

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“…they are biased. MTFs derived from such signals also fail to describe texture loss [12], [14]- [16] that is a primary driver of perceived capture system image quality [38].…”

Section: The Modulation Transfer Function (Mtf)mentioning

confidence: 99%

Validation of Modulation Transfer Functions and Noise Power Spectra from Natural Scenes

Fry¹,

Triantaphillidou²,

Jenkin³

et al. 2019

jist

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The Modulation Transfer Function (MTF) and the Noise Power Spectrum (NPS) characterize imaging system sharpness/resolution and noise, respectively. Both measures are based on linear system theory but are applied routinely to systems employing non-linear, content-aware image processing. For such systems, MTFs/NPSs are derived inaccurately from traditional test charts containing edges, sinusoids, noise or uniform tone signals, which are unrepresentative of natural scene signals. The dead leaves test chart delivers improved measurements, but still has limitations when describing the performance of scene-dependent systems. In this paper, we validate several novel scene-and-process-dependent MTF (SPD-MTF) and NPS (SPD-NPS) measures that characterize, either: i) system performance concerning one scene, or ii) average real-world performance concerning many scenes, or iii) the level of system scene-dependency. We also derive novel SPD-NPS and SPD-MTF measures using the dead leaves chart. We demonstrate that all the proposed measures are robust and preferable for scene-dependent systems than current measures.

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“…they are biased. MTFs derived from such signals also fail to describe texture loss [12], [14]- [16] that is a primary driver of perceived capture system image quality [38].…”

Section: The Modulation Transfer Function (Mtf)mentioning

confidence: 99%

Validation of Modulation Transfer Functions and Noise Power Spectra from Natural Scenes

Fry¹,

Triantaphillidou²,

Jenkin³

et al. 2019

jist

View full text Add to dashboard Cite

show abstract

“…The CPIQ's implementation (Figure 3) is more complex to execute and computationally intensive than STVIQMs, or CP-IQMs. Out of its seven univariate IQM components, those relating to spatial signal transfer and visual noise are most significant to overall perceived quality [5]. Both these components are covered by multivariate STV-IQMs.…”

Section: A Capture Systems' Engineering Perspective Of Iqmsmentioning

confidence: 99%

“…Sinusoid and edge targets describe limiting resolution and subjective sharpness respectively for these systems [3], but do not describe perceived texture loss effectively [4]. Texture loss is a primary driver of overall perceived image quality in today's non-linear capture systems [5].…”

Section: Modulation Transfer Function (Mtf)mentioning

confidence: 99%

“…Each attribute is modelled by a univariate metric in units of JND. 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.…”

Section: Multivariate Formalism (Mf-iqm)mentioning

confidence: 99%

“…MF-IQMs are used widely in capture system image quality assessment, and feature in recent ISO 20462 image quality ruler [34] and CPIQ IQM [5] standards. They are modular, adaptable and provide logical pathways towards their quality scores.…”

Section: A Capture Systems' Engineering Perspective Of Iqmsmentioning

confidence: 99%

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