Tone Mapped Image Quality Index Research Articles

Three-Stage Tone Mapping Algorithm

In this paper, a tone mapping algorithm is presented to map real-world luminance into displayed luminance. Our purpose is to reveal the local contrast of real-world scenes on a conventional monitor. Around this point, we propose a three-stage algorithm to visualize high dynamic range images. All pixels of high dynamic range images are classified into three groups. For the first stage, we introduce piecewise linear mapping as the global tone mapping operator to map the luminance of the first group, which provides overall impressions of luminance. For the second stage, the luminance of the second group is determined by the weighted average of its neighborhood pixels, which are derived from the first group’s pixels. For the third stage, the luminance of the third group is determined by the weighted average of its neighborhood pixels, which are derived from the second group’s pixels. Experimental results on several real-world images and the TMQI database show that our algorithm can improve the visibility of real-world scenes with about 12% and 9% higher scores of mean opinion score and tone-mapped image quality index than the closest competitive tone mapping methods. Compared to the existing tone mapping methods, our algorithm produces visually compelling results without halo artifacts and loss of detail.

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

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.

Comparison between Digital Tone-Mapping Operators and a Focal-Plane Pixel-Parallel Circuit

In a previous work, we proposed a color extension of an existing focal-plane tone-mapping operator (FPTMO) and introduced circuit modifications that led to smaller sensing array area and simpler off-chip white-balance operations. The proposed FPTMO chip was fabricated and its experimental test setup is under development. In the present work, we systematically compare the previously proposed FPTMOs with conventional digital tone-mapping operators (DTMOs), both in terms of overall image quality and execution time. To assess the image quality, we use the Tone-Mapped image Quality Index (TMQI) metric, the Blind Tone-Mapped Quality Index (BTMQI) metric, and an image colorfulness metric. By statistically comparing the results given by both metrics, we verify that the proposed FPTMOs achieve results similar to those obtained with DTMOs, occasionally outperforming them. To compare the tone-mapping operators execution times, we perform a detailed complexity analysis. Results show that system-level software FPTMO descriptions (FPTMO functionality described in software) rank among the fastest DTMOs and that the hardware FPTMO (described in software) speed-up depends on the target frame rate. For 30-frames-per-second color image generation, the fastest FPTMO is 15 times faster than the DTMO with highest TMQI and BTMQI metric scores, and 170 times faster than the DTMO with best colorfulness metric score.

Scene Segmentation-Based Luminance Adjustment for Multi-Exposure Image Fusion.

We propose a novel method for adjusting luminance for multi-exposure image fusion. For the adjustment, two novel scene segmentation approaches based on luminance distribution are also proposed. Multi-exposure image fusion is a method for producing images that are expected to be more informative and perceptually appealing than any of the input ones, by directly fusing photos taken with different exposures. However, existing fusion methods often produce unclear fused images when input images do not have a sufficient number of different exposure levels. In this paper, we point out that adjusting the luminance of input images makes it possible to improve the quality of the final fused images. This insight is the basis of the proposed method. The proposed method enables us to produce high-quality images, even when undesirable inputs are given. Visual comparison results show that the proposed method can produce images that clearly represent a whole scene. In addition, multi-exposure image fusion with the proposed method outperforms state-of-the-art fusion methods in terms of MEF-SSIM, discrete entropy, tone mapped image quality index, and statistical naturalness.

HDR tone mapping algorithm based on difference compression with adaptive reference values

Tone mapping remains a challenging problem since tone mapping operators need to produce high perceptual quality under all conditions. In this paper, we propose a new local tone mapping method based on difference compression with adaptive reference values, which can effectively reproduce the details of bright and shadow regions. We also use a global tone mapping method and blend the output images produced by the global and local methods based on objective quality metrics. To quantitatively measure output images, we developed a new objective quality metric for the tone mapped images. The proposed detailness metric measures detail loss in the bright and shadow regions, and shows good correlations with subjective quality. We combined this metric with the recently proposed tone mapped image quality index (TMQI) that may not sufficiently reflect the amount of local detail loss. The experiments show that the proposed algorithm provides better perceptual quality than existing methods.

High Dynamic Range Image Analysis through Various Tone Mapping Techniques

image quality is improved drastically with the increase of the technology. The conventional display devices may not be suitable for these High dynamic range images. The tone mapping is the process to show the good quality image in the normal LDR display devices. This paper presents a review of the tone mapping algorithms. It provides the methodology on Tone Mapped Image Quality Index (TMIQI) and the Blind Quality Assessment of Tone-Mapped Images (BTMQI). The region is basically expanded and compressed to visualize properly. Thereby the region-enhanced pseudo-exposures are fused into an HDR image. The image quality of BTMQI is comparatively higher than the TMIQI method. The low dynamic range images are suitable to both the conventional and advance display devices. Keywordsdynamic range imaging, structural preservation, tone mapping, perceptual image processing, structural similarity

Adaptive contrast enhancement using edge-based lighting condition estimation

This paper proposes a new approach to image contrast enhancement that improves the perceptual visual quality by considering the lighting condition and minimizing the structural distortion to a tolerable level. The proposed method consists of the following two major steps: lighting condition estimation and contrast enhancement processes. In the first step, the proposed method estimates the lighting condition by calculating the dynamic range along the edges of the image. In the second step, the method adaptively adjusts the luminance by considering both the estimated lighting condition and the order of luminance levels in order to improve the perceptual visual quality. In addition, the method properly reduces the structural distortion. Experimental results show that the proposed method improved the perceptual visual quality of various images by increasing the average structural fidelity, enhancement performance measure, entropy, and tone-mapped image quality index by up to 11%, 133%, 16%, and 11%, respectively, compared to the benchmark methods.

Union Laplacian pyramid with multiple features for medical image fusion

The Laplacian pyramid has been widely used for decomposing images into multiple scales. However, the Laplacian pyramid is believed as being unable to represent outline and contrast of the images well. To tackle these tasks, an approach union Laplacian pyramid with multiple features is presented for accurately transferring salient features from the input medical images into a single fused image. Firstly, the input images are transformed into their multi-scale representations by Laplacian pyramid. Secondly, the contrast feature map and outline feature map are extracted from the images at each scale, respectively. Thirdly, after extracting the multiple features, an efficient fusion scheme is developed to combine the pyramid coefficients. Lastly, the fused image is obtained by a reconstruction process of the inversed pyramid. Visual and statistical analyses show that the quality of fused image can be significantly improved over that of typical image quality assessment metrics in terms of structural similarity, peak-signal-to-noise ratio, standard deviation, and tone mapped image quality index metrics. The contrast is also well preserved by histogram analysis of images.

High dynamic range image compression by optimizing tone mapped image quality index.

Tone mapping operators (TMOs) aim to compress high dynamic range (HDR) images to low dynamic range (LDR) ones so as to visualize HDR images on standard displays. Most existing TMOs were demonstrated on specific examples without being thoroughly evaluated using well-designed and subject-validated image quality assessment models. A recently proposed tone mapped image quality index (TMQI) made one of the first attempts on objective quality assessment of tone mapped images. Here, we propose a substantially different approach to design TMO. Instead of using any predefined systematic computational structure for tone mapping (such as analytic image transformations and/or explicit contrast/edge enhancement), we directly navigate in the space of all images, searching for the image that optimizes an improved TMQI. In particular, we first improve the two building blocks in TMQI—structural fidelity and statistical naturalness components—leading to a TMQI-II metric. We then propose an iterative algorithm that alternatively improves the structural fidelity and statistical naturalness of the resulting image. Numerical and subjective experiments demonstrate that the proposed algorithm consistently produces better quality tone mapped images even when the initial images of the iteration are created by the most competitive TMOs. Meanwhile, these results also validate the superiority of TMQI-II over TMQI.

Evaluation of Various Tone Mapping Operators for Backward Compatible JPEG Image Coding

Recently, the standardization of backward compatible JPEG image coding for high dynamic range (HDR) image has been undertaken to establish an international standard called “JPEG XT.” The JPEG XT consists of two layers: the base layer and the residual layer. The base layer contains tone mapped low dynamic range (LDR) image data and the residual layer contains the error signal used to reconstruct the HDR image. This paper gives the result of a study to evaluate the overall performance of tone mapping operators (TMOs) for this standard. The evaluation is performed using five HDR image datasets and six TMOs for profiles A, B, and C of the proposed JPEG XT standard. The Tone Mapped image Quality Index (TMQI) and no reference image quality assessment (NR IQA) are used for measuring the LDR image quality. The peak signal to noise ratio (PSNR) is used to evaluate the overall compression performance of JPEG XT profiles A, B, and C. In TMQI and NR IQA measurements, TMOs using display adaptive tone mapping and adaptive logarithmic mapping each gave good results. A TMO using adaptive logarithmic mapping gave good PSNRs.

Color Image Enhancement Based on Adaptive Nonlinear Curves of Luminance Features

This paper proposes an image-dependent color image enhancement method that uses adaptive luminance enhancement and color emphasis. It effectively enhances details of low-light regions while maintaining well-balanced luminance and color information. To compare the structure similarity and naturalness, we used the tone mapped image quality index (TMQI). The proposed method maintained better structure similarity in the enhanced image than did the space-variant luminance map (SVLM) method or the adaptive and integrated neighborhood dependent approach for nonlinear enhancement (AINDANE). The proposed method required the smallest computation time among the three algorithms. The proposed method can be easily implemented using the field-programmable gate array (FPGA), with low hardware resources and with better performance in terms of similarity.

English

2 ABSTRACT: This paper deals with the assessment of measuring the quality of tone mapped images by considering the Tone Mapping Operators (TMOs) that convert high dynamic range (HDR) to low dynamic range (LDR) images provide practically useful tools for the visualization of HDR images on standard LDR displays. Different TMOs create different tone mapped images, and a natural question is which one has the best quality. Without an appropriate quality measure, different TMOs cannot be compared, and further improvement is directionless. Subjective rating may be a reliable evaluation method, but it is expensive and time consuming, and more importantly, is difficult to be embedded into optimization frameworks. Here we propose an objective quality assessment algorithm for tone mapped images by combining two concepts one of them is a multi scale signal fidelity measure on the basis of a modified structural similarity index and the other followed by a naturalness measure on the basis of intensity statistics of natural images. Validations using independent subject-rated image databases show good correlations between subjective ranking score and the proposed tone-mapped image quality index (TMQI). The proposed measure not only provides an overall quality score of an image, but also creates multi-scale quality maps that reflect the structural fidelity variations across scale and space.

Objective Quality Assessment of Tone-Mapped Images

Tone-mapping operators (TMOs) that convert high dynamic range (HDR) to low dynamic range (LDR) images provide practically useful tools for the visualization of HDR images on standard LDR displays. Different TMOs create different tone-mapped images, and a natural question is which one has the best quality. Without an appropriate quality measure, different TMOs cannot be compared, and further improvement is directionless. Subjective rating may be a reliable evaluation method, but it is expensive and time consuming, and more importantly, is difficult to be embedded into optimization frameworks. Here we propose an objective quality assessment algorithm for tone-mapped images by combining: 1) a multiscale signal fidelity measure on the basis of a modified structural similarity index and 2) a naturalness measure on the basis of intensity statistics of natural images. Validations using independent subject-rated image databases show good correlations between subjective ranking score and the proposed tone-mapped image quality index (TMQI). Furthermore, we demonstrate the extended applications of TMQI using two examples-parameter tuning for TMOs and adaptive fusion of multiple tone-mapped images.