Zonal brightness coherency for video tone mapping

Boitard, Ronan; Cozot, Rémi; Thoreau, Dominique; Bouatouch, Kadi

doi:10.1016/j.image.2013.10.001

Cited by 28 publications

(38 citation statements)

References 18 publications

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“…12 The result of the subjective evaluation of the four tested tone mapping algorithms. The figure shows from top to bottom the result using our method, Aydin et al [2], Boitard et al [7], and Eilertsen et al [14]. All methods give satisfactory results, with minor variations.…”

Section: Resultsmentioning

confidence: 74%

“…Of these we found that [35] and [44] Fig. 7 The result of running our Algorithm 2 on a number of HDR images. No parameters need to be set to produce the output.…”

Section: Results On Hdr Imagesmentioning

confidence: 99%

“…We have compared our results with three state-of-theart video tone mapping algorithms, the zonal brightness coherency method of Boitard et al [7], the temporally coher- Fig. 8 Four frames from the output of Algorithm 3 with the student sequence as input.…”

Section: Results On Hdr Videomentioning

confidence: 99%

“…A number of operators are based on ideas mimicking the human visual system, some of which are global, e.g., [16,21,39,55] and some local, e.g., [5,28]. Some recent methods use more involved local processing and filtering schemes to achieve high local contrast and also reduce noise [2,4,6,7,14]. For overview and evaluation of video tone mapping operators see [15,40].…”

Section: Related Workmentioning

confidence: 99%

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Temporally Consistent Tone Mapping of Images and Video Using Optimal K-means Clustering

Oskarsson

2016

J Math Imaging Vis

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The field of high dynamic range imaging addresses the problem of capturing and displaying the large range of luminance levels found in the world, using devices with limited dynamic range. In this paper we present a novel tone mapping algorithm that is based on K-means clustering. Using dynamic programming we are able to not only solve the clustering problem efficiently, but also find the global optimum. Our algorithm runs in O(N 2 K ) for an image with N input luminance levels and K output levels. We show that our algorithm gives comparable results to state-of-theart tone mapping algorithms, but with the additional large benefit of a minimum of parameters. We show how to extend the method to handle video input. We test our algorithm on a number of standard high dynamic range images and video sequences and give qualitative and quantitative comparisons to a number of state-of-the-art tone mapping algorithms.

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Section: Resultsmentioning

confidence: 74%

“…Of these we found that [35] and [44] Fig. 7 The result of running our Algorithm 2 on a number of HDR images. No parameters need to be set to produce the output.…”

Section: Results On Hdr Imagesmentioning

confidence: 99%

Section: Results On Hdr Videomentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

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Temporally Consistent Tone Mapping of Images and Video Using Optimal K-means Clustering

Oskarsson

2016

J Math Imaging Vis

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“…There are also techniques for imposing temporal coherence in tone-mapping as a post-processing step, alleviating flickering artifacts of arbitrary global TMOs [38,39,107]. For local coherence, motion estimation by means of e.g.…”

Section: Temporal Aspectsmentioning

confidence: 99%

The high dynamic range imaging pipeline : Tone-mapping, distribution, and single-exposure reconstruction

Eilertsen¹

2018

Linköping Studies in Science and Technology. Dissertations

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Vision models fine-tuned by cinema professionals for High Dynamic Range imaging in movies

Cyriac

Canham

Kane

et al. 2020

Multimed Tools Appl

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Many challenges that deal with processing of HDR material remain very much open for the film industry, whose extremely demanding quality standards are not met by existing automatic methods. Therefore, when dealing with HDR content, substantial work by very skilled technicians has to be carried out at every step of the movie production chain. Based on recent findings and models from vision science, we propose in this work effective tone mapping and inverse tone mapping algorithms for production, post-production and exhibition. These methods are automatic and real-time, and they have been both fine-tuned and validated by cinema professionals, with psychophysical tests demonstrating that the proposed algorithms outperform both the academic and industrial state-of-the-art. We believe these methods bring the field closer to having fully automated solutions for important challenges for the cinema industry that are currently solved manually or sub-optimally. Another contribution of our research is to highlight the limitations of existing image quality metrics when applied to the tone mapping problem, as none of them, including two state-of-the-art deep learning metrics for image perception, are able to predict the preferences of the observers.

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Zonal brightness coherency for video tone mapping

Cited by 28 publications

References 18 publications

Temporally Consistent Tone Mapping of Images and Video Using Optimal K-means Clustering

Temporally Consistent Tone Mapping of Images and Video Using Optimal K-means Clustering

The high dynamic range imaging pipeline : Tone-mapping, distribution, and single-exposure reconstruction

Vision models fine-tuned by cinema professionals for High Dynamic Range imaging in movies

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