Quality Evaluation of Color Demosaicing According to Image Resolution

Yang, Yanqin; Losson, Olivier; Duvieubourg, Luc

doi:10.1109/sitis.2007.33

Cited by 10 publications

(6 citation statements)

References 13 publications

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“…We also propose a measurement for the false color artifact (Yang et al, 2007). At a pixel in the demosaiced image, any mere error in the estimated value a color component can be considered as a false color.…”

Section: False Colors Measurementmentioning

confidence: 99%

Comparison of Color Demosaicing Methods

Losson¹,

Macaire²,

Yang³

2010

Advances in Imaging and Electron Physics

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Section: False Colors Measurementmentioning

confidence: 99%

Comparison of Color Demosaicing Methods

Losson¹,

Macaire²,

Yang³

2010

Advances in Imaging and Electron Physics

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“…This article is intended to establish a path forward for future research investigating possible relationships between CFA demosaicing and color image superresolution. Reference 17 shows that the quality of a demosaiced image decreases with the resolution of the raw data, suggesting possible gains from super-resolving a raw image sequence before demosaicing. With new machinery for comparing image quality without a reference image, a study investigating ties between CFA demosaicing and color image super-resolution is now within reach.…”

Section: Resultsmentioning

confidence: 99%

Review of Bayer pattern CFA demosaicing with new quality assessment algorithms

et al. 2010

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Given the frequent lack of a reference image or ground truth when performance testing Bayer pattern color filter array (CFA) demosaicing algorithms, two new no-reference quality assessment algorithms are proposed. These new quality assessment algorithms give a relative comparison of two demosaicing algorithms by measuring the presence of two common artifacts in their output images. For this purpose, various demosaicing algorithms are reviewed, especially adaptive color plane, gradient based methods, and median filtering, with particular attention paid to the false color and edge blurring artifacts common to all demosaicing algorithms. Classic quality assessment methods which require a reference image, such as MSE, PSNR, and ΔE, are reviewed, their typical usage characterized, and their associated pitfalls identified. With this information in mind, the motivations for no-reference quality assessment are discussed. The new quality assessment algorithms are then designed for a relative comparison of two images demosaiced from the same CFA data by measuring the sharpness of the edges and determining the presence of false colors. Demosaicing algorithms described earlier are evaluated and ranked using these new algorithms. A large quantity of real images is given for review. These images are also used to justify those rankings suggested by the new quality assessment algorithms. This work provides a path forward for future research investigating possible relationships between CFA demosaicing and color image super-resolution.

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“…In [15] a FR algorithm for false color quantification is proposed, where the absolute difference between the pixel values of the demosaiced and corresponding original image is computed for the R, G, and B color planes, then the maximum color difference value among the three color planes is found. Subsequently, if that difference is greater than a specified threshold value, then that pixel is considered as being affected by the false color artifact.…”

Section: ) Use the Prewitt Operator To Find Edge Points In The Gaussmentioning

confidence: 99%

No-Reference Perceived Image Quality Algorithm for Demosaiced Images

Lamb¹,

Khambete²

2016

ijacsa

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Abstract-Visual image quality assessment (IQA) plays a key role in every multimedia application, as end user to it is a human-being. Real time applications demand no reference (NR) IQA, due to unavailability of the reference image. Today, most of the perceived/visual NR-IQA algorithms developed are for distortions like blur, ringing, and blocking artifacts. Very few are available for color distortions. Visible color distortions, such as false color, and zipper are produced in the demosaiced image due to incorrect interpolation of missing color values. In this paper, state of the art zipper and false color artifact quantification algorithms, general purpose NR-IQA algorithms are evaluated for visual quality assessment of demosaiced images. Separate NR-IQA algorithms are proposed for zipper and false color artifact quantification scores are then combined to obtain final quality score for demosaiced image. Zipper algorithm quantifies zipper artifact by searching for zipper pixels in an image, while false color algorithm finds correlation between local high frequency region's color planes to quantify false color.

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Quality Evaluation of Color Demosaicing According to Image Resolution

Cited by 10 publications

References 13 publications

Comparison of Color Demosaicing Methods

Comparison of Color Demosaicing Methods

Review of Bayer pattern CFA demosaicing with new quality assessment algorithms

No-Reference Perceived Image Quality Algorithm for Demosaiced Images

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