High Dynamic Range Image Compression by Optimizing Tone Mapped Image Quality Index

Ma, Kede; Yeganeh, Hojatollah; Zeng, Kai; Wang, Zhou

doi:10.1109/tip.2015.2436340

Cited by 112 publications

(8 citation statements)

References 29 publications

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“…We include in our comparisons fourteen tone mapping operators including ten traditional methods which for simplicity we refer to as: Mantiuk [MMS06], Shan [SJB09], Durand [DD02], Drago [DMAC03], Mertens [MKVR07], Reinhard [RSSF02], Ma [MYZW15], Liang [LXZ ∗ 18], Shibata [STO16], and Li [LJZ18]; and four recent learning‐based methods: Guo [GJ21], Zhang [ZZWW21], DeepTMO [RSV ∗ 19] and TMO‐Net [PKO ∗ 21]. We use the publicly available implementations of these methods or if not available, their implementation in HDRToolBox [BADC17].…”

Section: Results and Ablation Studymentioning

confidence: 99%

“…This allows for both small details and large image structures to be preserved. Ma et al [MYZW15] propose an iterative algorithm that directly optimizes the resulting tone mapped image to maximize structural fidelity and statistical naturalness following a new tone mapping quality metric based on TMQI. Liang et al [LXZ ∗ 18] design a hybrid l 1 ‐ l 0 multi‐scale decomposition model that decomposes the image into a base layer, to which an l 1 sparsity term is imposed to enforce piecewise smoothness, and a detail layer, to which an l 0 sparsity term is imposed as structural prior, in order to avoid halos and over‐enhancement of contrast.…”

Section: Related Workmentioning

confidence: 99%

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Learning a self‐supervised tone mapping operator via feature contrast masking loss

Wang

Chen

Seidel

et al. 2022

Computer Graphics Forum

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High Dynamic Range (HDR) content is becoming ubiquitous due to the rapid development of capture technologies. Nevertheless, the dynamic range of common display devices is still limited, therefore tone mapping (TM) remains a key challenge for image visualization. Recent work has demonstrated that neural networks can achieve remarkable performance in this task when compared to traditional methods, however, the quality of the results of these learning‐based methods is limited by the training data. Most existing works use as training set a curated selection of best‐performing results from existing traditional tone mapping operators (often guided by a quality metric), therefore, the quality of newly generated results is fundamentally limited by the performance of such operators. This quality might be even further limited by the pool of HDR content that is used for training. In this work we propose a learning‐based self‐supervised tone mapping operator that is trained at test time specifically for each HDR image and does not need any data labeling. The key novelty of our approach is a carefully designed loss function built upon fundamental knowledge on contrast perception that allows for directly comparing the content in the HDR and tone mapped images. We achieve this goal by reformulating classic VGG feature maps into feature contrast maps that normalize local feature differences by their average magnitude in a local neighborhood, allowing our loss to account for contrast masking effects. We perform extensive ablation studies and exploration of parameters and demonstrate that our solution outperforms existing approaches with a single set of fixed parameters, as confirmed by both objective and subjective metrics.

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Section: Results and Ablation Studymentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Learning a self‐supervised tone mapping operator via feature contrast masking loss

Wang

Chen

Seidel

et al. 2022

Computer Graphics Forum

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show abstract

“…After mixed method and sigma filtering, to prevent artificial image distortion, the three indexes of mean square error α, peak signal to noise ratio β, and one-dimensional image entropy γ are used to test and evaluate the image quality after the process of image enhancement [31][32][33]. The calculation formulas are given below.…”

Section: Quality Evaluation Index Of Image Enhancement Effectmentioning

confidence: 99%

Underwater Image Mosaic Algorithm Based on Improved Image Registration

Zhao

Gao

et al. 2021

Applied Sciences

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In order to obtain panoramic images in a low contrast underwater environment, an underwater panoramic image mosaic algorithm based on image enhancement and improved image registration (IIR) was proposed. Firstly, mixed filtering and sigma filtering are used to enhance the contrast of the original image and de-noise the image. Secondly, scale-invariant feature transform (SIFT) is used to detect image feature points. Then, the proposed IIR algorithm is applied to image registration to improve the matching accuracy and reduce the matching time. Finally, the weighted smoothing method is used for image fusion to avoid image seams. The results show that IIR algorithm can effectively improve the registration accuracy, shorten the registration time, and improve the image fusion effect. In the field of cruise research, instruments equipped with imaging systems, such as television capture and deep-drag camera systems, can produce a large number of image or video recordings. This algorithm provides support for fast and accurate underwater image mosaic and has important practical significance.

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“…Such subjective evaluation takes a lot of time and energy, with the results unstable across different participant groups. 27 Another solution is objective metrics, e.g., tone-mapped image quality index (TMQI) 28 and TMQI-II, 29 widely used in tone-mapping optimization studies. 6,30 TMQI represents a form of indexing that considers the naturalness of tone-mapped LDR images, and structural fidelity between the HDR and tone-mapped LDR images expressed as 28 E Q -T A R G E T ; t e m p : i n t r a l i n k -; e 0 0 3 ; 1 1 6 ; 6 0 4 TMQIðH; LÞ ¼ a½SðH; LÞ α þ ð1 − aÞ½NðLÞ β ;…”

Section: As Followsmentioning

confidence: 99%

Adversarial and adaptive tone mapping operator: multi-scheme generation and multi-metric evaluation

et al. 2021

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Tone mapping is one of the main techniques to convert high-dynamic range (HDR) images into low-dynamic range (LDR) images. We propose to use a variant of generative adversarial networks to adaptively tone map images. We designed a conditional adversarial generative network composed of a U-Net generator and patchGAN discriminator to adaptively convert HDR images into LDR images. We extended previous work to include additional metrics such as tone-mapped image quality index (TMQI), structural similarity index measure, Fréchet inception distance, and perceptual path length. In addition, we applied face detection on the Kalantari dataset and showed that our proposed adversarial tone mapping operator generates the best LDR image for the detection of faces. One of our training schemes, trained via 256 × 256 resolution HDR-LDR image pairs, results in a model that can generate high TMQI low-resolution 256 × 256 and high-resolution 1024 × 2048 LDR images. Given 1024 × 2048 resolution HDR images, the TMQI of the generated LDR images reaches a value of 0.90, which outperforms all other contemporary tone mapping operators. © The Authors. Published by SPIE under a Creative Commons Attribution 4.0 Unported License. Distribution or reproduction of this work in whole or in part requires full attribution of the original publication, including its DOI.

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High Dynamic Range Image Compression by Optimizing Tone Mapped Image Quality Index

Cited by 112 publications

References 29 publications

Learning a self‐supervised tone mapping operator via feature contrast masking loss

Learning a self‐supervised tone mapping operator via feature contrast masking loss

Underwater Image Mosaic Algorithm Based on Improved Image Registration

Adversarial and adaptive tone mapping operator: multi-scheme generation and multi-metric evaluation

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