Quality Of HDR Images Research Articles

Adaptive fusion of multi-exposure images based on perceptron model

Abstract Multi-exposure image fusion as a technical means to bridge the dynamic range gap between real scenes and image acquisition devices, which makes the fused images better quality and more realistic and vivid simulation of real scenes, has been widely concerned by scholars from various countries. In order to improve the adaptive fusion effect of multi-exposure images, this paper proposes a fusion algorithm based on multilayer perceptron (MLP) based on the perceptron model and verifies the feasibility of the algorithm by the peak signal-to-noise ratio (PSNR), correlation coefficient (PCC), structural similarity (SSMI) and HDR-VDR-2, an evaluation index of HDR image quality. Comparison with other algorithms revealed that the average PSNR of the MLP algorithm improved by 4.43% over the Ma algorithm, 7.88% over the Vanmail algorithm, 10.30% over the FMMR algorithm, 11.19% over the PMF algorithm, and 11.19% over the PMF algorithm. For PCC, the MLP algorithm improves by 20.14%, 17.46%, 2.31%, 11.24%, and 15.36% over the other algorithms in that order. For SSMI, the MLP algorithm improved by 16.99%, 8.96%, 17.17%, 14.41%, and 4.85% over the other algorithms, in that order. For HDR-VDR-2, the MLP algorithm improved by 3.02%, 2.79%, 6.84%, 4.90%, and 6.55% over the other algorithms, in that order. The results show that the MLP algorithm can avoid image artifacts while retaining more details. The MLP-based adaptive fusion method is a step further in the theoretical study of multi-exposure image fusion, which is of great significance for subsequent research and practical application by related technology vendors.

Combining Quality Metrics using Machine Learning for improved and robust HDR Image Quality Assessment

Combining Quality Metrics using Machine Learning for improved and robust HDR Image Quality Assessment

Objective HDR image quality assessment

Although there is a lot of literature about generating HDR images, very limited research has been conducted on the issue of HDR image quality based on people’s feeling and preferences. The goal of this paper is to identify an objective quality measurement of an HDR image. The key features of HDR images that affect people’s preferences are uncovered through a survey in which testers judge different sample images of different scenes. The experimental results show that the proposed quality measurement for HDR images generates scores consistent with the true feelings of observers.

Assessment of multi-exposure HDR image deghosting methods

To avoid motion artefacts when merging multiple exposures into a high dynamic range image, a number of HDR deghosting algorithms have been proposed. However, these algorithms do not work equally well on all types of scenes, and some may even introduce additional artefacts. As the number of proposed deghosting methods is increasing rapidly, there is an immediate need to evaluate them and compare their results. Even though subjective methods of evaluation provide reliable means of testing, they are often cumbersome and need to be repeated for each new proposed method or even its slight modification. Because of that, there is a need for objective quality metrics that will provide automatic means of evaluation of HDR deghosting algorithms. In this work, we explore several computational approaches of quantitative evaluation of multi-exposure HDR deghosting algorithms and demonstrate their results on five state-of-the-art algorithms. In order to perform a comprehensive evaluation, a new dataset consisting of 36 scenes has been created, where each scene provides a different challenge for a deghosting algorithm. The quality of HDR images produced by deghosting method is measured in a subjective experiment and then evaluated using objective metrics. As this paper is an extension of our conference paper, we add one more objective quality metric, UDQM, as an additional metric in the evaluation. Furthermore, analysis of objective and subjective experiments is performed and explained more extensively in this work. By testing correlation between objective metric and subjective scores, the results show that from the tested metrics, that HDR-VDP-2 is the most reliable metric for evaluating HDR deghosting algorithms. The results also show that for most of the tested scenes, Sen et al.’s deghosting method outperforms other evaluated deghosting methods. The observations based on the obtained results can be used as a vital guide in the development of new HDR deghosting algorithms, which would be robust to a variety of scenes and could produce high quality results.

Objective Estimation Methods for the Quality of HDR Images and Their Evaluation with Subjective Assessment

Objective Estimation Methods for the Quality of HDR Images and Their Evaluation with Subjective Assessment

Dynamic Backlight Gamma on High Dynamic Range LCD TVs

A high dynamic range liquid crystal display (HDR-LCD) can enhance the contrast ratio of images by utilizing locally controlled dynamic backlight. We studied the HDR-LCD as a dual-panel display: a backlight module and a liquid crystal (LC) cell. As the gamma of the LC signal, the backlight module was also endowed with a gamma function to control the contrast ratio of HDR images. The inverse of a mapping function (IMF) method proposed as a dynamic gamma mapping curve for the backlight module, has been demonstrated to further improve in HDR image quality. By implementing the IMF method on a HDR-LCD TV with 88 backlight zones, the image contrast ratio can reach while maintaining high brightness, clear image detail, and an average power reduction of 30%.

39.4: Inverse of Mapping Function (IMF) Method for Image Quality Enhancement of High Dynamic Range LCD TVs

AbstractA high dynamic range liquid crystal display (HDR‐LCD) can much enhance the contrast ratio of the image on a locally controlled dynamic backlight. A novel method, inverse of mapping function (IMF), was demonstrated to further improve the HDR image quality from the information of each frame. By using the IMF method, the image contrast ratio in a 37″ HDR‐LCD TV was successfully improved to ∼20,000:1 while maintaining high brightness and clear image details. Besides, for mass production purpose, the backlight of a 37″ HDR‐LCD TV was divide into 8×8 zones to reduce hardware computation complexity.