Optimized Tone Mapping of HDR Images via HVS Model-Based 2D Histogram Equalization

Nguyen, Nam Hoang; Vo, Tu; Jeong, Younghoon; Moon, Yiu Sang; Lee, Chul

doi:10.23919/apsipa.2018.8659452

Cited by 3 publications

(8 citation statements)

References 15 publications

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“…We evaluate the performance of the proposed tone-mapping algorithm in comparison with those of Mantiuk et al's display adaptive tone-mapping algorithm [12], Song and Cosman's content-dependence tone-mapping algorithm [15], and Nguyen et al's HVS response model-based tone-mapping algorithm [16]. Note that these algorithms were developed by exploiting the display characteristics.…”

Section: Resultsmentioning

confidence: 99%

“…We implemented Song and Cosman's algorithm [15] with 10bit bit-depth and used the EOTF as the display model. The source code for Nguyen et al's algorithm [16] was provided by the authors.…”

Section: A Experimental Settingsmentioning

confidence: 99%

“…Song and Cosman [15] considered the display contrast and human visual adaptation to construct the tone-mapping function, thereby enhancing the luminance and visibility of details. Nguyen et al [16] proposed an HVS response model-based tone-mapping algorithm to minimize the visual response difference between an HDR image and its tone-mapped image. Although these global tone-mapping algorithms are computationally efficient in general, they may fail to preserve the local details in an input HDR image, especially in regions of high contrast.…”

Section: Introductionmentioning

confidence: 99%

“…Meanwhile, the luminance enhancement tone-mapping algorithm [15] considers only the image characteristics based on the pixel value distribution, without the textual details. The HVS response model-based tone-mapping algorithm [16] uses a guided tone-mapping curve (TMC) to preserve the absolute luminance that may cause detail loss in the bright regions. We also note that, although recent deep learning-based algorithms have shown significant tone-mapping performance improvements [23]- [27], they too have been developed without considering the characteristics of the display devices.…”

Section: Introductionmentioning

confidence: 99%

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Human Visual System Model-Based Optimized Tone Mapping of High Dynamic Range Images

Nguyen

Lee

2021

IEEE Access

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High dynamic range (HDR) image and video technology can provide significant picture quality improvement over the standard dynamic range (SDR). However, when HDR content is represented on an SDR display, dynamic range compression may result in image quality deterioration. To address this problem, we propose an optimized human visual system (HVS) response model-based tone-mapping algorithm to preserve the perceptual responses between the HDR image and its tone-mapped image. First, we measure the HVS response differences using a 2D histogram when an HDR image is displayed on an HDR device and when its tone-mapped image is displayed on an SDR device. Then, we formulate an optimization problem to minimize the differences. By efficiently solving the optimization problem, we obtain an optimal tone-mapping curve. Experimental results on actual displays demonstrate that the proposed algorithm provides superior image quality compared with conventional algorithms in terms of both subjective and objective evaluations.INDEX TERMS High dynamic range, HDR10, tone-mapping, human visual system, contrast sensitivity, optimization.

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Section: Resultsmentioning

confidence: 99%

“…We implemented Song and Cosman's algorithm [15] with 10bit bit-depth and used the EOTF as the display model. The source code for Nguyen et al's algorithm [16] was provided by the authors.…”

Section: A Experimental Settingsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Human Visual System Model-Based Optimized Tone Mapping of High Dynamic Range Images

Nguyen

Lee

2021

IEEE Access

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“…Moreover, the problem of allocation of excessive display levels to the larger regions is solved by excluding the visually indistinguishable pixels from counting. Nguyen et al [6] formed histograms of HDR and LDR images and used an HVS model to minimize the difference between the two to improve the quality of tone-mapped images. Han et al [7] incorporated the effect of ambient light on the HVS in the method of Khan et al [5], to generate tone-mapped images suitable for different viewing environments.…”

Section: Introductionmentioning

confidence: 99%

Tone-Mapping Using Perceptual-Quantizer and Image Histogram

2020

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A new tone-mapping algorithm is presented for visualization of high dynamic range (HDR) images on low dynamic range (LDR) displays. In the first step, the real-world pixel intensities of the HDR image are transformed to a perceptual domain using the perceptual-quantizer (PQ). This is followed by construction of the histogram of the luminance channel. Tone-mapping curve is generated from the cumulative histogram. It is known that histogram-based tone-mapping approaches can lead to excessive stretching of contrast in highly populated bins, whereas the pixels in sparse bins can suffer from excessive compression of contrast. We handle these issues by restricting the pixel counts in the histogram to remain below a defined limit, determined by a uniform distribution model. The proposed method is compared with state-of-the-art algorithms, using some well-known metrics that quantify the quality of tone-mapped images, and is found to have the best performance. INDEX TERMS Image enhancement, high dynamic range imaging, tone-mapping, image visualization.

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Unified implementation of global high dynamic range image tone-mapping algorithms

Khan

Rahardja

2022

MBE

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<abstract> <p>High dynamic range (HDR) images and video require tone-mapping for display on low dynamic range (LDR) screens. Many tone-mapping operators have been proposed to convert HDR content to LDR, but almost each has a different implementation structure and requires a different execution time. We propose a unified structure that can represent any global tone-mapping algorithm with an array of just 256 coefficients. These coefficients extracted offline for every HDR image or video frame can be used to convert them to LDR in real time using linear interpolation. The produced LDR images are identical to the images produced by the original implementation of the algorithm. This unified implementation can replicate any global tone-mapping function and requires very low and fixed execution time, which is independent of algorithm and type of content and depends only on image size. Experimental studies are presented to show the accuracy and time efficiency of the proposed implementation.</p> </abstract>

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Optimized Tone Mapping of HDR Images via HVS Model-Based 2D Histogram Equalization

Cited by 3 publications

References 15 publications

Human Visual System Model-Based Optimized Tone Mapping of High Dynamic Range Images

Human Visual System Model-Based Optimized Tone Mapping of High Dynamic Range Images

Tone-Mapping Using Perceptual-Quantizer and Image Histogram

Unified implementation of global high dynamic range image tone-mapping algorithms

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