Fully-automatic inverse tone mapping algorithm based on dynamic mid-level tone mapping

Luzardo, Gonzalo; Aelterman, Jan; Luong, Hiêp; Rousseaux, Sven; Ochoa, Daniel; Philips, Wilfried

doi:10.1017/atsip.2020.5

Cited by 7 publications

(7 citation statements)

References 48 publications

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“…In this paper, we present comprehensive inverse tone mapping, artifact suppression, and a highlight enhancement pipeline for video sequences designed to address the challenges described above. This work significantly advances our earlier contributions, strategically extending our inverse tone mapping approach for LDR images, initially introduced in [13,32], and seamlessly integrating it with our decontouring algorithm detailed in [31] to formulate a comprehensive LDR-to-HDR conversion pipeline optimized for video content. Furthermore, we introduce new steps for artifact suppression and a novel step for highlight enhancement, with the explicit aim of elevating the quality of the resultant HDR video sequence.…”

mentioning

confidence: 84%

“…Traditionally, iTM methods have been designed to create HDR images with the best subjective quality when viewed on HDR screens. These methods vary in complexity and approach, ranging from simple techniques that retain key attributes of the LDR content such as contrast and color [8][9][10][11][12][13][14], to more complex machine learning-based techniques [15][16][17][18][19][20][21]. In addition, other LDR-to-HDR conversion methods, known as single-image HDR reconstruction (SI-HDR) methods, have recently emerged.…”

Section: Oledmentioning

confidence: 99%

“…The core of our proposed inverse tone mapping pipeline is the dynamic range expansion step. For this, we use our fully automatic inverse tone mapping method based on the dynamic mid-level tone mapping proposed in [13,32].…”

Section: Dynamic Range Expansionmentioning

confidence: 99%

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A Display-Adaptive Pipeline for Dynamic Range Expansion of Standard Dynamic Range Video Content

Luzardo,

Kumcu,

Aelterman

et al. 2024

Applied Sciences

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Recent advancements in high dynamic range (HDR) display technology have significantly enhanced the contrast ratios and peak brightness of modern displays. In the coming years, it is expected that HDR televisions capable of delivering significantly higher brightness and, therefore, contrast levels than today’s models will become increasingly accessible and affordable to consumers. While HDR technology has gained prominence over the past few years, low dynamic range (LDR) content is still consumed due to a substantial volume of historical multimedia content being recorded and preserved in LDR. Although the amount of HDR content will continue to increase as HDR becomes more prevalent, a large portion of multimedia content currently remains in LDR. In addition, it is worth noting that although the HDR standard supports multimedia content with luminance levels up to 10,000 cd/m2 (a standard measure of brightness), most HDR content is typically limited to a maximum brightness of around 1000 cd/m2. This limitation aligns with the current capabilities of consumer HDR TVs but is a factor approximately five times brighter than current LDR TVs. To accurately present LDR content on a HDR display, it is processed through a dynamic range expansion process known as inverse tone mapping (iTM). This LDR to HDR conversion faces many challenges, including the inducement of noise artifacts, false contours, loss of details, desaturated colors, and temporal inconsistencies. This paper introduces complete inverse tone mapping, artifact suppression, and a highlight enhancement pipeline for video sequences designed to address these challenges. Our LDR-to-HDR technique is capable of adapting to the peak brightness of different displays, creating HDR video sequences with a peak luminance of up to 6000 cd/m2. Furthermore, this paper presents the results of comprehensive objective and subjective experiments to evaluate the effectiveness of the proposed pipeline, focusing on two primary aspects: real-time operation capability and the quality of the HDR video output. Our findings indicate that our pipeline enables real-time processing of Full HD (FHD) video (1920 × 1080 pixels), even on hardware that has not been optimized for this task. Furthermore, we found that when applied to existing HDR content, typically capped at a brightness of 1000 cd/m2, our pipeline notably enhances its perceived quality when displayed on a screen that can reach higher peak luminances.

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confidence: 84%

Section: Oledmentioning

confidence: 99%

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A Display-Adaptive Pipeline for Dynamic Range Expansion of Standard Dynamic Range Video Content

Luzardo,

Kumcu,

Aelterman

et al. 2024

Applied Sciences

Self Cite

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“…Currently, few subjective studies [19,43,59,[81][82][83] are designed for SDR-to-HDR procedure (rather between different HDR). Similar to [19], we judge if output HDR is better than origin SDR.…”

Section: Subjective Experimentsmentioning

confidence: 99%

Learning a Practical SDR-to-HDRTV Up-conversion using New Dataset and Degradation Models

Guo¹,

Fan²,

Xue³

et al. 2023

Preprint

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In media industry, the demand of SDR-to-HDRTV upconversion arises when users possess HDR-WCG (high dynamic range-wide color gamut) TVs while most off-the-shelf footage is still in SDR (standard dynamic range). The research community has started tackling this low-level vision task by learning-based approaches. When applied to real SDR, yet, current methods tend to produce dim and desaturated result, making nearly no improvement on viewing experience. Different from other network-oriented methods, we attribute such deficiency to training set (HDR-SDR pair). Consequently, we propose new HDRTV dataset (dubbed HDRTV4K) and new HDR-to-SDR degradation models. Then, it's used to train a luminance-segmented network (LSN) consisting of a global mapping trunk, and two Transformer branches on bright and dark luminance range. We also update assessment criteria by tailored metrics and subjective experiment. Finally, ablation studies are conducted to prove the effectiveness. Our work is available at: https://github.com/AndreGuo/HDRTVDM .

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“…The merging process requires carefully removing the effect of moving objects and many techniques have been proposed for this purpose [11][12]. In the current state of imaging technology, high dynamic range of natural scenes can be captured quite reliably, and the recent focus is mainly on improving the speed of operation and reconstruction of HDR from a single LDR image using deep learning approaches [13].…”

Section: Introductionmentioning

confidence: 99%

Differential Evolution-based Approach for Tone-Mapping of High Dynamic Range Images

Almaghthawi¹,

Bourennani²,

Rasool³

2020

IJACSA

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Recently, high dynamic range (HDR) imaging has received significant attention from research community as well as the industrial companies due to valuable applications of HDR images in better visualization and analysis. However, HDR images need to be converted to low dynamic range (LDR) images for viewing on standard LDR display screens. Several tonemapping operators have been proposed for the conversion, however, so far, no significant works have been reported employing artificial intelligence to achieve better enhancement of the output images. In this paper, we present an optimizationbased approach, to enhance the quality of the tone-mapped LDR images using metaheuristics. More specifically, the optimization process is based on the differential evolution (DE) algorithm which takes tone-mapping function of an existing histogrambased method as initial guess and refines the histogram bins iteratively leading to progressive enhancement of the quality of LDR image. The final results produced by the proposed optimized histogram-based approach (OHbA) showed better performance compared to the existing state-of-the-art tonemapping algorithms.

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Fully-automatic inverse tone mapping algorithm based on dynamic mid-level tone mapping

Cited by 7 publications

References 48 publications

A Display-Adaptive Pipeline for Dynamic Range Expansion of Standard Dynamic Range Video Content

A Display-Adaptive Pipeline for Dynamic Range Expansion of Standard Dynamic Range Video Content

Learning a Practical SDR-to-HDRTV Up-conversion using New Dataset and Degradation Models

Differential Evolution-based Approach for Tone-Mapping of High Dynamic Range Images

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