&lt;title&gt;Further image quality assessment in digital film restoration&lt;/title&gt;

Chambah, Majed; Saint-Jean, Christophe; Helt, Francois; Rizzi, Alessandro

doi:10.1117/12.642414

Cited by 5 publications

(6 citation statements)

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“…Fig. 7 shows the application of our algorithm to film restoration [26], [28], [29]. In this example, a frame from an old 8-mm film positive shows a very strong red cast.…”

Section: Methodsmentioning

confidence: 96%

“…For that, we use the notation introduced in Section III, and we write the energies in a discrete framework. Let be two images, where represents the initial data, and are symmetric functions such that (28) We consider the discrete functional ; that is, we replace the image by its regularized version where is convolution kernel such as a Gaussian, i.e., see Appendix G.…”

Section: On the Existence Of Minima Ofmentioning

confidence: 99%

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Perceptual Color Correction Through Variational Techniques

Bertalmío

Caselles

Provenzi

et al. 2007

IEEE Trans. on Image Process.

159

272

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In this paper, we present a discussion about perceptual-based color correction of digital images in the framework of variational techniques. We propose a novel image functional whose minimization produces a perceptually inspired color enhanced version of the original. The variational formulation permits a more flexible local control of contrast adjustment and attachment to data. We show that a numerical implementation of the gradient descent technique applied to this energy functional coincides with the equation of automatic color enhancement (ACE), a particular perceptual-based model of color enhancement. Moreover, we prove that a numerical approximation of the Euler-Lagrange equation reduces the computational complexity of ACE from theta(N2) to theta(N log N), where N is the total number of pixels in the image.

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“…Fig. 7 shows the application of our algorithm to film restoration [26], [28], [29]. In this example, a frame from an old 8-mm film positive shows a very strong red cast.…”

Section: Methodsmentioning

confidence: 96%

Section: On the Existence Of Minima Ofmentioning

confidence: 99%

Perceptual Color Correction Through Variational Techniques

Bertalmío

Caselles

Provenzi

et al. 2007

IEEE Trans. on Image Process.

159

272

View full text Add to dashboard Cite

show abstract

“…An example of this are all the video steady algorithms, which zoom in on the anchor point of the image and cut the pixels at the edge of frames to reduce camera movement (Figure 2). Furthermore, different digital restoration procedures (eg, scratch removal) produce artefacts on the output, which can distract the public from the subject of a scene even more than the original defect would have 10 . These problems in the digital restoration pipeline are known about in the field, but the typical solution for dealing with them is to perform all the operations manually then visually controlling the result.…”

Section: Digital Restorationmentioning

confidence: 99%

Research directions in color movie restoration

Plutino

Rizzi

2020

Coloration Technology

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“…This explains why in some domains such as photography and old film restoration, where there is no reference to compare to, subjective quality evaluation is the most reliable technique used. In the last works we presented some original reference free metrics [22][23][24][25] that correlated well with human perception (metrics for contrast and color quality based on statistical and perceptual approaches). These perception correlated metrics were a first step to bridge the gap between subjective and automatic approaches.…”

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confidence: 98%

Toward an automatic subjective image quality assessment system

et al. 2009

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Usually in the field of image quality assessment the terms "automatic" and "subjective" are often incompatible. In fact, when it comes to image quality assessment, we have mostly two kinds of evaluation techniques: subjective evaluation and objective evaluation. Only objective evaluation techniques being automatizable, while subjective evaluation techniques are performed by a series of visual assessment done by expert or non-expert observers. In this paper, we will present a first attempt to an automatic subjective quality assessment system. The system computes some perception correlated color metrics from a learning set of images. During the learning stage a subjective assessment by users is required so that the system matches the subjective opinions with computed metrics on a variety of images. Once the learning process is over, the system operates in an automatic mode using only the learned knowledge and the reference free computed metrics from the images to assess. Results and also future prospects of this work are presented. CONTEXTWhen it comes to image quality assessment, we have mostly two kinds of evaluation techniques [20] : subjective evaluation and objective evaluation. These evaluations can be done with a priori information (with reference) or without use of a priori information (reference free). Subjective approaches, which to date are the only widely recognized method of determining actual perceived quality, are complex and time-consuming, both in their preparation and execution [21]. Subjective evaluation is formalized with defined procedures [20]. Objective quality evaluation use metrics to evaluate image quality. Objective evaluation is automated, hence it costs less than a subjective evaluation, plus it can be done in real-time since it needs no user interaction. Objective quality metrics can be full reference, reduced reference or reference free [21] Moreover, automatic objective assessment systems do not necessarily correlate well with perceived quality [20,21]. Ideally, a quality assessment system would perceive and measure image or video impairments just like a human being. As long as quality metrics are not correlated with human perception, subjective evaluation is still mandatory. This explains why in some domains such as photography and old film restoration, where there is no reference to compare to, subjective quality evaluation is the most reliable technique used. In the last works we presented some original reference free metrics [22][23][24][25] that correlated well with human perception (metrics for contrast and color quality based on statistical and perceptual approaches). These perception correlated metrics were a first step to bridge the gap between subjective and automatic approaches. In fact, since perceptual correlated metrics are available, automatic computation and learning -and hence automatic assessment-become possible.In this paper, we will present a first attempt to an automatic subjective quality assessment system. The system computes some perception correlated color me...

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<title>Further image quality assessment in digital film restoration</title>

Cited by 5 publications

References 0 publications

Perceptual Color Correction Through Variational Techniques

Perceptual Color Correction Through Variational Techniques

Research directions in color movie restoration

Toward an automatic subjective image quality assessment system

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