Tone Mapping Operator for High Dynamic Range Images Based on Modified iCAM06

Li, Yumei; Liao, Ningfang; Wu, Weiming; Deng, Chenyang; Li, Yasheng; Fan, Qiumei; Liu, Chuanjie

doi:10.3390/s23052516

Cited by 4 publications

(6 citation statements)

References 33 publications

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“…In the research literature, the vast majority of state-of-the-art tone mapping methods are optimized for enhancement of the image appearance for visualization purposes, based on models of the human visual system (HVS) or for subjective quality and artistic photography purposes [ 7 , 8 , 9 , 10 , 11 , 12 , 13 , 14 , 15 , 16 , 17 ]. The majority of these methods consider either local or global image features, but the most successful approaches typically analyze the input on multiple scales [ 9 ].…”

Section: Related Workmentioning

confidence: 99%

“…Nevertheless, ranking tone-mapping methods is not straightforward and depends on the evaluation criteria, as found by [ 11 , 12 ]. A recent method, iCAM06-m [ 10 ], builds upon the perceptual appearance model iCAM06 [ 8 ], and focuses on improving the color representation of tone-mapped HDR images. Subjective and objective evaluation experiments were carried out to evaluate the perceptual image quality of the results, achieving slightly better results for the modified model.…”

Section: Related Workmentioning

confidence: 99%

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High-Dynamic-Range Tone Mapping in Intelligent Automotive Systems

Shopovska

Stojkovic

Aelterman

et al. 2023

Sensors

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Intelligent driver assistance systems are becoming increasingly popular in modern passenger vehicles. A crucial component of intelligent vehicles is the ability to detect vulnerable road users (VRUs) for an early and safe response. However, standard imaging sensors perform poorly in conditions of strong illumination contrast, such as approaching a tunnel or at night, due to their dynamic range limitations. In this paper, we focus on the use of high-dynamic-range (HDR) imaging sensors in vehicle perception systems and the subsequent need for tone mapping of the acquired data into a standard 8-bit representation. To our knowledge, no previous studies have evaluated the impact of tone mapping on object detection performance. We investigate the potential for optimizing HDR tone mapping to achieve a natural image appearance while facilitating object detection of state-of-the-art detectors designed for standard dynamic range (SDR) images. Our proposed approach relies on a lightweight convolutional neural network (CNN) that tone maps HDR video frames into a standard 8-bit representation. We introduce a novel training approach called detection-informed tone mapping (DI-TM) and evaluate its performance with respect to its effectiveness and robustness in various scene conditions, as well as its performance relative to an existing state-of-the-art tone mapping method. The results show that the proposed DI-TM method achieves the best results in terms of detection performance metrics in challenging dynamic range conditions, while both methods perform well in typical, non-challenging conditions. In challenging conditions, our method improves the detection F2 score by 13%. Compared to SDR images, the increase in F2 score is 49%.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

High-Dynamic-Range Tone Mapping in Intelligent Automotive Systems

Shopovska

Stojkovic

Aelterman

et al. 2023

Sensors

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“…However, global TMOs may result in insufficient local contrast, leading to the loss of some local details. Local TMOs, such as TMOs based on Retinex theory [13], the image color appearance model [14], and the TMO proposed in [15], adjust the tone curve based on local information around a pixel. These TMOs have been extensively developed to meet the requirements of many applications.…”

Section: Introductionmentioning

confidence: 99%

A Low-Latency Noise-Aware Tone Mapping Operator for Hardware Implementation with a Locally Weighted Guided Filter

Liang,

Yan,

Wang

et al. 2024

Symmetry

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A tone mapping operator (TMO) is a module in the image signal processing pipeline that is used to convert high dynamic range images to low dynamic range images for display. Currently, state-of-the-art TMOs typically take complex algorithms and are implemented on graphics processing units, making it difficult to run with low latency on edge devices, and TMOs implemented in hardware circuits often lack additional noise suppression because of latency and hardware resource constraints. To address these issues, we proposed a low-latency noise-aware TMO for hardware implementation. Firstly, a locally weighted guided filter is proposed to decompose the luminance image into a base layer and a detail layer, with the weight function symmetric concerning the central pixel value of a window. Secondly, the mean and standard deviation of the basic layer and the detail layer are used to estimate the noise visibility according to the human visual characteristics. Finally, the gain for the detail layer is calculated to achieve adaptive noise suppression. In this process, luminance is first processed by the log2 function before being filtered and then symmetrically converted back to the linear domain by the exp2 function after compression. Meanwhile, the algorithms within the proposed TMO were optimized for hardware implementation to minimize latency and cache, achieving a low latency of 60.32 μs under video specification of 1080 P at 60 frames per second and objective metric smoothness in dark flat regions could be improved by more than 10% compared to similar methods.

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“…Many TMOs have been proposed over the years [5][6][7][8][9][10][11][12]. The TMOs are based on image processing techniques, HVS, or sigmoidal functions.…”

Section: Introductionmentioning

confidence: 99%

“…Li et al enhanced iCAM06 by adding a multi-scale enhancement algorithm to correct the chroma of images by compensating for saturation and hue drift [5]. However, the compensated chroma and hue factors were depended on two parameters to be tuned per image.…”

Section: Introductionmentioning

confidence: 99%

Perceptual Tone Mapping Model for High Dynamic Range Imaging

Mehmood,

Shi,

Khan

et al. 2023

IEEE Access

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One of the key challenges in tone mapping is to preserve the perceptual quality of high dynamic range (HDR) images when mapping them to standard dynamic range (SDR) displays. Traditional tone mapping operators (TMOs) compress the luminance of HDR images without considering the surround and display conditions emanating into suboptimal results. Current research addresses this challenge by incorporating perceptual color appearance attributes. In this work, we propose a TMO (TMOz) that leverages CIECAM16 perceptual attributes, i.e., brightness, colorfulness, and hue. TMOz accounts for the effects of both the surround and the display conditions to achieve more optimal colorfulness reproduction. The perceptual brightness is compressed, and the perceptual color scales, i.e., colorfulness and hue are derived from HDR images by employing CIECAM16 color adaptation equations. A psychophysical experiment was conducted to automate the brightness compression parameter. The model employs fully automatic and adaptive approach, obviating the requirement for manual parameter selection. TMOz was evaluated in terms of contrast, colorfulness and overall image quality. The objective and subjective evaluation methods revealed that the proposed model outperformed the state-of-the-art TMOs.INDEX TERMS CIECAM16, high dynamic range, TMOz, tone mapping, perceptual tone mapping

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Tone Mapping Operator for High Dynamic Range Images Based on Modified iCAM06

Cited by 4 publications

References 33 publications

High-Dynamic-Range Tone Mapping in Intelligent Automotive Systems

High-Dynamic-Range Tone Mapping in Intelligent Automotive Systems

A Low-Latency Noise-Aware Tone Mapping Operator for Hardware Implementation with a Locally Weighted Guided Filter

Perceptual Tone Mapping Model for High Dynamic Range Imaging

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