Showing papers on "Image quality published in 2015"

A Feature-Enriched Completely Blind Image Quality Evaluator

Abstract: Existing blind image quality assessment (BIQA) methods are mostly opinion-aware. They learn regression models from training images with associated human subjective scores to predict the perceptual quality of test images. Such opinion-aware methods, however, require a large amount of training samples with associated human subjective scores and of a variety of distortion types. The BIQA models learned by opinion-aware methods often have weak generalization capability, hereby limiting their usability in practice. By comparison, opinion-unaware methods do not need human subjective scores for training, and thus have greater potential for good generalization capability. Unfortunately, thus far no opinion-unaware BIQA method has shown consistently better quality prediction accuracy than the opinion-aware methods. Here, we aim to develop an opinion-unaware BIQA method that can compete with, and perhaps outperform, the existing opinion-aware methods. By integrating the features of natural image statistics derived from multiple cues, we learn a multivariate Gaussian model of image patches from a collection of pristine natural images. Using the learned multivariate Gaussian model, a Bhattacharyya-like distance is used to measure the quality of each image patch, and then an overall quality score is obtained by average pooling. The proposed BIQA method does not need any distorted sample images nor subjective quality scores for training, yet extensive experiments demonstrate its superior quality-prediction performance to the state-of-the-art opinion-aware BIQA methods. The MATLAB source code of our algorithm is publicly available at www.comp.polyu.edu.hk / $\sim $ cslzhang/IQA/ILNIQE/ILNIQE.htm.

An Underwater Color Image Quality Evaluation Metric

Abstract: Quality evaluation of underwater images is a key goal of underwater video image retrieval and intelligent processing. To date, no metric has been proposed for underwater color image quality evaluation (UCIQE). The special absorption and scattering characteristics of the water medium do not allow direct application of natural color image quality metrics especially to different underwater environments. In this paper, subjective testing for underwater image quality has been organized. The statistical distribution of the underwater image pixels in the CIELab color space related to subjective evaluation indicates the sharpness and colorful factors correlate well with subjective image quality perception. Based on these, a new UCIQE metric, which is a linear combination of chroma, saturation, and contrast, is proposed to quantify the non-uniform color cast, blurring, and low-contrast that characterize underwater engineering and monitoring images. Experiments are conducted to illustrate the performance of the proposed UCIQE metric and its capability to measure the underwater image enhancement results. They show that the proposed metric has comparable performance to the leading natural color image quality metrics and the underwater grayscale image quality metrics available in the literature, and can predict with higher accuracy the relative amount of degradation with similar image content in underwater environments. Importantly, UCIQE is a simple and fast solution for real-time underwater video processing. The effectiveness of the presented measure is also demonstrated by subjective evaluation. The results show better correlation between the UCIQE and the subjective mean opinion score.

Using free energy principle for blind image quality assessment

Abstract: In this paper we propose a new no-reference (NR) image quality assessment (IQA) metric using the recently revealed free-energy-based brain theory and classical human visual system (HVS)-inspired features. The features used can be divided into three groups. The first involves the features inspired by the free energy principle and the structural degradation model. Furthermore, the free energy theory also reveals that the HVS always tries to infer the meaningful part from the visual stimuli. In terms of this finding, we first predict an image that the HVS perceives from a distorted image based on the free energy theory, then the second group of features is composed of some HVS-inspired features (such as structural information and gradient magnitude) computed using the distorted and predicted images. The third group of features quantifies the possible losses of “naturalness” in the distorted image by fitting the generalized Gaussian distribution to mean subtracted contrast normalized coefficients. After feature extraction, our algorithm utilizes the support vector machine based regression module to derive the overall quality score. Experiments on LIVE, TID2008, CSIQ, IVC, and Toyama databases confirm the effectiveness of our introduced NR IQA metric compared to the state-of-the-art.

Perceptual Quality Assessment for Multi-Exposure Image Fusion

Abstract: Multi-exposure image fusion (MEF) is considered an effective quality enhancement technique widely adopted in consumer electronics, but little work has been dedicated to the perceptual quality assessment of multi-exposure fused images. In this paper, we first build an MEF database and carry out a subjective user study to evaluate the quality of images generated by different MEF algorithms. There are several useful findings. First, considerable agreement has been observed among human subjects on the quality of MEF images. Second, no single state-of-the-art MEF algorithm produces the best quality for all test images. Third, the existing objective quality models for general image fusion are very limited in predicting perceived quality of MEF images. Motivated by the lack of appropriate objective models, we propose a novel objective image quality assessment (IQA) algorithm for MEF images based on the principle of the structural similarity approach and a novel measure of patch structural consistency. Our experimental results on the subjective database show that the proposed model well correlates with subjective judgments and significantly outperforms the existing IQA models for general image fusion. Finally, we demonstrate the potential application of the proposed model by automatically tuning the parameters of MEF algorithms. 1 The subjective database and the MATLAB code of the proposed model will be made available online. Preliminary results of Section III were presented at the 6th International Workshop on Quality of Multimedia Experience , Singapore, 2014.

Blind image quality evaluation using perception based features

Abstract: This paper proposes a novel no-reference Perception-based Image Quality Evaluator (PIQUE) for real-world imagery. A majority of the existing methods for blind image quality assessment rely on opinion-based supervised learning for quality score prediction. Unlike these methods, we propose an opinion unaware methodology that attempts to quantify distortion without the need for any training data. Our method relies on extracting local features for predicting quality. Additionally, to mimic human behavior, we estimate quality only from perceptually significant spatial regions. Further, the choice of our features enables us to generate a fine-grained block level distortion map. Our algorithm is competitive with the state-of-the-art based on evaluation over several popular datasets including LIVE IQA, TID & CSIQ. Finally, our algorithm has low computational complexity despite working at the block-level.

Information processing method and electronic equipment

Abstract: The invention provides an information processing method and electronic equipment. The information processing method and the electronic equipment are used for settling a technical problem of improving an image with an overlow contrast in prior art and realizes a technical effect of improving an image quality through effective image contrast increase. The information processing method comprises the steps of acquiring a first image of which the first contrast is lower than a threshold; acquiring the first color value of at least one pixel point of the first image, wherein the first color value is composed of the value of each color channel of a first color space; calculating the color deviation of the first image and a reinforcing coefficient to be processed on the first image; acquiring a new first color value of at least one pixel point based on the first color value of at least one pixel point, the color deviation and the reinforcing coefficient; and acquiring a first optimized image which corresponds with the first image based on the new first color value of at least one pixel point.

Blind Image Quality Assessment via Deep Learning

Abstract: This paper investigates how to blindly evaluate the visual quality of an image by learning rules from linguistic descriptions. Extensive psychological evidence shows that humans prefer to conduct evaluations qualitatively rather than numerically. The qualitative evaluations are then converted into the numerical scores to fairly benchmark objective image quality assessment (IQA) metrics. Recently, lots of learning-based IQA models are proposed by analyzing the mapping from the images to numerical ratings. However, the learnt mapping can hardly be accurate enough because some information has been lost in such an irreversible conversion from the linguistic descriptions to numerical scores. In this paper, we propose a blind IQA model, which learns qualitative evaluations directly and outputs numerical scores for general utilization and fair comparison. Images are represented by natural scene statistics features. A discriminative deep model is trained to classify the features into five grades, corresponding to five explicit mental concepts, i.e., excellent, good, fair, poor, and bad. A newly designed quality pooling is then applied to convert the qualitative labels into scores. The classification framework is not only much more natural than the regression-based models, but also robust to the small sample size problem. Thorough experiments are conducted on popular databases to verify the model’s effectiveness, efficiency, and robustness.

Methods for differential image quality enhancement for a multiple detector system, systems and use thereof

Abstract: Methods for differential image quality enhancement for a detection system including multiple electromagnetic radiation detectors which include obtaining an image from a chemical band electromagnetic radiation detector and an image from a reference band electromagnetic radiation detector. Each of the images includes a plurality of pixels, each pixel having an associated intensity value. One or more intensity values of a plurality of pixels from the reference band image are adjusted based on one or more intensity value parameters of the chemical band image.

A Patch-Structure Representation Method for Quality Assessment of Contrast Changed Images

Abstract: Contrast is a fundamental attribute of images that plays an important role in human visual perception of image quality With numerous approaches proposed to enhance image contrast, much less work has been dedicated to automatic quality assessment of contrast changed images Existing approaches rely on global statistics to estimate contrast quality Here we propose a novel local patch-based objective quality assessment method using an adaptive representation of local patch structure, which allows us to decompose any image patch into its mean intensity, signal strength and signal structure components and then evaluate their perceptual distortions in different ways A unique feature that differentiates the proposed method from previous contrast quality models is the capability to produce a local contrast quality map, which predicts local quality variations over space and may be employed to guide contrast enhancement algorithms Validations based on four publicly available databases show that the proposed patch-based contrast quality index (PCQI) method provides accurate predictions on the human perception of contrast variations

No-Reference Quality Assessment of Contrast-Distorted Images Based on Natural Scene Statistics

Abstract: Contrast distortion is often a determining factor in human perception of image quality, but little investigation has been dedicated to quality assessment of contrast-distorted images without assuming the availability of a perfect-quality reference image. In this letter, we propose a simple but effective method for no-reference quality assessment of contrast distorted images based on the principle of natural scene statistics (NSS). A large scale image database is employed to build NSS models based on moment and entropy features. The quality of a contrast-distorted image is then evaluated based on its unnaturalness characterized by the degree of deviation from the NSS models. Support vector regression (SVR) is employed to predict human mean opinion score (MOS) from multiple NSS features as the input. Experiments based on three publicly available databases demonstrate the promising performance of the proposed method.

Deep Multi-patch Aggregation Network for Image Style, Aesthetics, and Quality Estimation

Abstract: This paper investigates problems of image style, aesthetics, and quality estimation, which require fine-grained details from high-resolution images, utilizing deep neural network training approach. Existing deep convolutional neural networks mostly extracted one patch such as a down-sized crop from each image as a training example. However, one patch may not always well represent the entire image, which may cause ambiguity during training. We propose a deep multi-patch aggregation network training approach, which allows us to train models using multiple patches generated from one image. We achieve this by constructing multiple, shared columns in the neural network and feeding multiple patches to each of the columns. More importantly, we propose two novel network layers (statistics and sorting) to support aggregation of those patches. The proposed deep multi-patch aggregation network integrates shared feature learning and aggregation function learning into a unified framework. We demonstrate the effectiveness of the deep multi-patch aggregation network on the three problems, i.e., image style recognition, aesthetic quality categorization, and image quality estimation. Our models trained using the proposed networks significantly outperformed the state of the art in all three applications.

A new image quality metric for image fusion: The sum of the correlations of differences

Abstract: Measuring the quality of fused images is a very important stage for image fusion applications. The fused image must comprise maximum information from each of source images of the same scene taken by different sensors. Therefore, the amount of the information gathered in the fused images reflects the quality of them. This paper proposes a new quality metric by making use of this knowledge. The metric employed to compare the performance of different image fusion algorithms. In the experiments, subjective correspondence of the proposed metric and several well-known metrics are compared. Experimental results show the feasibility of the developed metric.

Accurate calculation of computer-generated holograms using angular-spectrum layer-oriented method.

Abstract: Fast calculation and correct depth cue are crucial issues in the calculation of computer-generated hologram (CGH) for high quality three-dimensional (3-D) display. An angular-spectrum based algorithm for layer-oriented CGH is proposed. Angular spectra from each layer are synthesized as a layer-corresponded sub-hologram based on the fast Fourier transform without paraxial approximation. The proposed method can avoid the huge computational cost of the point-oriented method and yield accurate predictions of the whole diffracted field compared with other layer-oriented methods. CGHs of versatile formats of 3-D digital scenes, including computed tomography and 3-D digital models, are demonstrated with precise depth performance and advanced image quality.

Massive Online Crowdsourced Study of Subjective and Objective Picture Quality

Abstract: Most publicly available image quality databases have been created under highly controlled conditions by introducing graded simulated distortions onto high-quality photographs. However, images captured using typical real-world mobile camera devices are usually afflicted by complex mixtures of multiple distortions, which are not necessarily well-modeled by the synthetic distortions found in existing databases. The originators of existing legacy databases usually conducted human psychometric studies to obtain statistically meaningful sets of human opinion scores on images in a stringently controlled visual environment, resulting in small data collections relative to other kinds of image analysis databases. Towards overcoming these limitations, we designed and created a new database that we call the LIVE In the Wild Image Quality Challenge Database, which contains widely diverse authentic image distortions on a large number of images captured using a representative variety of modern mobile devices. We also designed and implemented a new online crowdsourcing system, which we have used to conduct a very large-scale, multi-month image quality assessment subjective study. Our database consists of over 350000 opinion scores on 1162 images evaluated by over 7000 unique human observers. Despite the lack of control over the experimental environments of the numerous study participants, we demonstrate excellent internal consistency of the subjective dataset. We also evaluate several top-performing blind Image Quality Assessment algorithms on it and present insights on how mixtures of distortions challenge both end users as well as automatic perceptual quality prediction models.

CID2013: A Database for Evaluating No-Reference Image Quality Assessment Algorithms

Abstract: This paper presents a new database, CID2013, to address the issue of using no-reference (NR) image quality assessment algorithms on images with multiple distortions. Current NR algorithms struggle to handle images with many concurrent distortion types, such as real photographic images captured by different digital cameras. The database consists of six image sets; on average, 30 subjects have evaluated 12–14 devices depicting eight different scenes for a total of 79 different cameras, 480 images, and 188 subjects (67% female). The subjective evaluation method was a hybrid absolute category rating-pair comparison developed for the study and presented in this paper. This method utilizes a slideshow of all images within a scene to allow the test images to work as references to each other. In addition to mean opinion score value, the images are also rated using sharpness, graininess, lightness, and color saturation scales. The CID2013 database contains images used in the experiments with the full subjective data plus extensive background information from the subjects. The database is made freely available for the research community.

CT radiation dose and iterative reconstruction techniques.

Abstract: Key Points 1 CT radiation dose optimization is one of the major concerns for the scientific community 2 CT image quality is dependent on the selected image reconstruction algorithm 3 Iterative reconstruction algorithms have reemerged with the potential of radiation dose optimization by lowering image noise 4 Tube current is the most common parameter used to reduce radiation dose along with iterative reconstruction 5 Tube potential (kV) is also used for dose optimization with iterative reconstruction in CT angiography protocols and small patients

PET Image Reconstruction Using Kernel Method

Abstract: Image reconstruction from low-count positron emission tomography (PET) projection data is challenging because the inverse problem is ill-posed. Prior information can be used to improve image quality. Inspired by the kernel methods in machine learning, this paper proposes a kernel based method that models PET image intensity in each pixel as a function of a set of features obtained from prior information. The kernel-based image model is incorporated into the forward model of PET projection data and the coefficients can be readily estimated by the maximum likelihood (ML) or penalized likelihood image reconstruction. A kernelized expectation-maximization algorithm is presented to obtain the ML estimate. Computer simulations show that the proposed approach can achieve better bias versus variance trade-off and higher contrast recovery for dynamic PET image reconstruction than the conventional maximum likelihood method with and without post-reconstruction denoising. Compared with other regularization-based methods, the kernel method is easier to implement and provides better image quality for low-count data. Application of the proposed kernel method to a 4-D dynamic PET patient dataset showed promising results.

HDR-VDP-2.2: a calibrated method for objective quality prediction of high-dynamic range and standard images

Abstract: With the emergence of high-dynamic range (HDR) imaging, the existing visual signal processing systems will need to deal with both HDR and standard dynamic range (SDR) signals. In such systems, computing the objective quality is an important aspect in various optimization processes (e.g., video encoding). To that end, we present a newly calibrated objective method that can tackle both HDR and SDR signals. As it is based on the previously proposed HDR-VDP-2 method, we refer to the newly calibrated metric as HDR-VDP-2.2. Our main contribution is toward improving the frequency-based pooling in HDR-VDP-2 to enhance its objective quality prediction accuracy. We achieve this by formulating and solving a constrained optimization problem and thereby finding the optimal pooling weights. We also carried out extensive cross-validation as well as verified the performance of the new method on independent databases. These indicate clear improvement in prediction accuracy as compared with the default pooling weights. The source codes for HDR-VDP-2.2 are publicly available online for free download and use.

Simultaneous estimation of image quality and distortion via multi-task convolutional neural networks

Abstract: In this work we describe a compact multi-task Convolutional Neural Network (CNN) for simultaneously estimating image quality and identifying distortions. CNNs are natural choices for multi-task problems because learned convolutional features may be shared by different high level tasks. However, we empirically argue that simply appending additional tasks based on the state of the art structure (e.g., [1]) does not lead to optimal solutions. We design a compact structure with nearly 90% fewer parameters compared to [1], and demonstrate its learning power.

Objective image quality assessment of 3D synthesized views

Abstract: Depth-Image-Based-Rendering (DIBR) techniques are essential for three-dimensional (3D) video applications such as 3D Television (3DTV) and Free-Viewpoint Video. However, this process is based on 3D warping and can induce serious distortions whose impact on the perceived quality is far different from the one experienced in the 2D imaging processes. Since quality evaluation of DIBR-synthesized views is fundamental for the design of perceptually friendly 3D video systems, an appropriate objective quality metric targeting the assessment of DIBR-synthesized views is momentous. Most of the 2D objective quality metrics fail in assessing the visual quality of DIBR-synthesized views because they have not been conceived for addressing the specificities of DIBR-related distortions. In this paper, a new full-reference objective quality metric, 3DSwIM (3D Synthesized view Image Quality Metric), dedicated to artifacts detection in DIBR-synthesized view-points is presented. The proposed scheme relies on a comparison of statistical features of wavelet subbands of two input images: the original image and the DIBR-based synthesized image. A registration step is included before the comparison step so that best matching blocks are always compared to ensure "shifting-resilience". In addition, a skin detection step weights the final quality score in order to penalize distorted blocks containing "skin-pixels" based on the assumption that a human observer is most sensitive to impairments affecting human subjects. Experimental tests show that the proposed method outperforms the conventional 2D and DIBR-dedicated quality metrics under test. HighlightsThis paper presents a new full- reference objective quality metric dedicated to artifacts detection in 3D synthesized views.The proposed metric is based on the comparison of statistical features of wavelet subbands of the original image and the DIBR-based synthesized image."Shifting-resilience" is granted by the use of a registration algorithm.The final quality score is weighted depending on the presence of "skin-pixels" based on the assumption that a human observer is more sensitive to impairments affecting human subjects.Experimental tests show that the proposed method outperforms the 2D conventional and DIBR-synthesized views dedicated quality metrics under test.

Improved layer-based method for rapid hologram generation and real-time interactive holographic display applications

Abstract: Layer-based method has been proposed as an efficient approach to calculate holograms for holographic image display. This paper further improves its calculation speed and depth cues quality by introducing three different techniques, an improved coding scheme, a multilayer depth- fused 3D method and a fraction method. As a result the total computation time is reduced more than 4 times, and holographic images with accommodation cue are calculated in real time to interactions with the displayed image in a proof-of-concept setting of head-mounted holographic displays.

Reversible data hiding scheme based on exploiting modification direction with two steganographic images

Abstract: In this paper, we propose a novel data hiding scheme with reversibility based on exploiting modification direction (EMD). One cover image is first chosen and prepared to generate two visually similar steganographic images. During the secret embedding, the pixels in the first steganographic image are modified by no more than one gray level to embed secret data using the traditional EMD method, while the pixels in the second steganographic image are adaptively modified through referring to the first steganographic image without any confusions in image recovery process. On the receiver side, secret data can be extracted easily and the original cover image can also be recovered from the two steganographic images correctly. Experimental results demonstrate that our scheme can achieve high hiding capacity and satisfactory visual quality.

Combining Ordered Subsets and Momentum for Accelerated X-Ray CT Image Reconstruction

Abstract: Statistical X-ray computed tomography (CT) reconstruction can improve image quality from reduced dose scans, but requires very long computation time. Ordered subsets (OS) methods have been widely used for research in X-ray CT statistical image reconstruction (and are used in clinical PET and SPECT reconstruction). In particular, OS methods based on separable quadratic surrogates (OS-SQS) are massively parallelizable and are well suited to modern computing architectures, but the number of iterations required for convergence should be reduced for better practical use. This paper introduces OS-SQS-momentum algorithms that combine Nesterov's momentum techniques with OS-SQS methods, greatly improving convergence speed in early iterations. If the number of subsets is too large, the OS-SQS-momentum methods can be unstable, so we propose diminishing step sizes that stabilize the method while preserving the very fast convergence behavior. Experiments with simulated and real 3D CT scan data illustrate the performance of the proposed algorithms.

Quality Prediction of Asymmetrically Distorted Stereoscopic 3D Images

Abstract: Objective quality assessment of distorted stereoscopic images is a challenging problem, especially when the distortions in the left and right views are asymmetric. Existing studies suggest that simply averaging the quality of the left and right views well predicts the quality of symmetrically distorted stereoscopic images, but generates substantial prediction bias when applied to asymmetrically distorted stereoscopic images. In this paper, we first build a database that contains both single-view and symmetrically and asymmetrically distorted stereoscopic images. We then carry out a subjective test, where we find that the quality prediction bias of the asymmetrically distorted images could lean toward opposite directions (overestimate or underestimate), depending on the distortion types and levels. Our subjective test also suggests that eye dominance effect does not have strong impact on the visual quality decisions of stereoscopic images. Furthermore, we develop an information content and divisive normalization-based pooling scheme that improves upon structural similarity in estimating the quality of single-view images. Finally, we propose a binocular rivalry-inspired multi-scale model to predict the quality of stereoscopic images from that of the single-view images. Our results show that the proposed model, without explicitly identifying image distortion types, successfully eliminates the prediction bias, leading to significantly improved quality prediction of the stereoscopic images. 1 1 Some partial preliminary results of this work were presented at International Workshop on Video Processing and Quality Metrics for Consumer Electronics, Chandler, AZ, Jan., 2014. and IEEE International Conference on Multimedia and Expo, Chengdu, China, July, 2014.

Learning Curves in Emergency Ultrasound Education

Abstract: Objectives Proficiency in the use of bedside ultrasound (US) has become standard in emergency medicine residency training. While milestones have been established for this training, supporting data for minimum standard experience are lacking. The objective of this study was to characterize US learning curves to identify performance plateaus for both image acquisition and interpretation, as well as compare performance characteristics of learners to those of expert sonographers. Methods A retrospective review of an US database was conducted at a single academic institution. Each examination was scored for agreement between the learner and expert reviewer interpretation and given a score for image quality. A locally weighted scatterplot smoothing method was used to generate a model of predicted performance for each individual examination type. Performance characteristics for expert sonographers at the site were also tracked and used in addition to performance plateaus as benchmarks for learning curve analysis. Results There were 52,408 US examinations performed between May 2007 and January 2013 and included for analysis. Performance plateaus occurred at different points for different US protocols, from 18 examinations for soft tissue image quality to 90 examinations for right upper quadrant image interpretation. For the majority of examination types, a range of 50 to 75 examinations resulted in both excellent interpretation (sensitivity > 84% and specificity > 90%) and good image quality (90% the image quality benchmark of expert sonographers). Conclusions Educational performance benchmarks occur at variable points for image interpretation and image quality for different examination types. These data should be considered when developing training standards for US education as well as experience requirements for US credentialing.

Free‐breathing pediatric MRI with nonrigid motion correction and acceleration

Abstract: Purpose To develop and assess motion correction techniques for high-resolution pediatric abdominal volumetric magnetic resonance images acquired free-breathing with high scan efficiency. Materials and Methods First, variable-density sampling and radial-like phase-encode ordering were incorporated into the 3D Cartesian acquisition. Second, intrinsic multichannel butterfly navigators were used to measure respiratory motion. Lastly, these estimates are applied for both motion-weighted data-consistency in a compressed sensing and parallel imaging reconstruction, and for nonrigid motion correction using a localized autofocusing framework. With Institutional Review Board approval and informed consent/assent, studies were performed on 22 consecutive pediatric patients. Two radiologists independently scored the images for overall image quality, degree of motion artifacts, and sharpness of hepatic vessels and the diaphragm. The results were assessed using paired Wilcoxon test and weighted kappa coefficient for interobserver agreements. Results The complete procedure yielded significantly better overall image quality (mean score of 4.7 out of 5) when compared to using no correction (mean score of 3.4, P < 0.05) and to using motion-weighted accelerated imaging (mean score of 3.9, P < 0.05). With an average scan time of 28 seconds, the proposed method resulted in comparable image quality to conventional prospective respiratory-triggered acquisitions with an average scan time of 91 seconds (mean score of 4.5). Conclusion With the proposed methods, diagnosable high-resolution abdominal volumetric scans can be obtained from free-breathing data acquisitions. J. Magn. Reson. Imaging 2015;42:407–420.

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

Abstract: 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. 1 1 Partial preliminary results of this work were presented at ICASSP 2013 and ICME 2014.