Subjective effects of white-balancing errors in digital photography

Nikkanen, Jarno; Gerasimow, Timo; Kong, Lingjia

doi:10.1117/1.3013232

Cited by 14 publications

(1 citation statement)

References 10 publications

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“…In a camera benchmarking study, typical number of different cameras (test images) is from 10 to 15. Test images are various reconstructions of a scene [11,22] that differ in dimensions such as sharpness, brightness and color [16,21]. Moreover, image processing in cameras introduces computational enhancement operations, such as sharpening, noise removal and color boosting.…”

Section: Methods For Evaluating Image Qualitymentioning

confidence: 99%

A new method for evaluating the subjective image quality of photographs: dynamic reference

Nuutinen

Virtanen

Leisti

et al. 2014

Multimed Tools Appl

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The Dynamic Reference (DR) method has been developed for subjective image quality experiments in which original or undistorted images are unavailable. The DR method creates reference image series from test images. Reference images are presented to observers as a slide show prior to evaluating their quality. As the observers view the set of reference images, they determine the overall variation in quality within the set of test images. This study compared the performance of the DR method to that of the standardized absolute category rating (ACR) and paired comparison (PC) methods. We measured the performance of each method in terms of time effort and discriminability. The results showed that the DR method is faster than the PC method and more accurate than the ACR method. The DR method is especially suitable for experiments that require highly accurate results in a short time.

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Section: Methods For Evaluating Image Qualitymentioning

confidence: 99%

A new method for evaluating the subjective image quality of photographs: dynamic reference

Nuutinen

Virtanen

Leisti

et al. 2014

Multimed Tools Appl

View full text Add to dashboard Cite

show abstract

Color Constancy

Fernández-Maloigne¹,

and²,

Macaire³

2012

Digital Color

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Applying Visual Object Categorization and Memory Colors for Automatic Color Constancy

Rahtu

Nikkanen

Kannala

et al. 2009

Image Analysis and Processing – ICIAP 2009

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This paper presents a framework for using high-level visual information to enhance the performance of automatic color constancy algorithms. The approach is based on recognizing special visual object categories, called here as memory color categories, which have a relatively constant color (e.g. the sky). If such category is found from image, the initial white balance provided by a low-level color constancy algorithm can be adjusted so that the observed color of the category moves toward the desired color. The magnitude and direction of the adjustment is controlled by the learned characteristics of the particular category in the chromaticity space. The object categorization is performed using bag-offeatures method and raw camera data with reduced preprocessing and resolution. The proposed approach is demonstrated in experiments involving the standard gray-world and the state-of-the-art gray-edge color constancy methods. In both cases the introduced approach improves the performance of the original methods.

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Subjective effects of white-balancing errors in digital photography

Cited by 14 publications

References 10 publications

A new method for evaluating the subjective image quality of photographs: dynamic reference

A new method for evaluating the subjective image quality of photographs: dynamic reference

Color Constancy

Applying Visual Object Categorization and Memory Colors for Automatic Color Constancy

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