In recent years, High Dynamic Range (HDR) imaging has attracted significant attention from industry and academia. As a result, there are currently several on-going efforts towards standardization and benchmarking of existing tools for HDR image and video, and one of the key aspects is that of video quality measurement (both subjective and objective approaches). Therefore, this paper aims to identify few key challenges in the said area and then discuss existing solutions. Specifically, we first discuss a few important practical aspects that make HDR video quality measurement potentially challenging. Second, we report our recent efforts towards developing HDR video datasets that have been subjectively annotated for visual quality. Finally, we analyze and compare the effectiveness of existing solutions for objective quality prediction.
Dynamic range compression (or tone mapping) of HDR content is an essential step towards rendering it on traditional LDR displays in a meaningful way. This is however non-trivial and one of the reasons is that tone mapping operators (TMOs) usually need content-specific parameters to achieve the said goal. While subjective TMO parameter adjustment is the most accurate, it may not be easily deployable in many practical applications. Its subjective nature can also influence the comparison of different operators. Thus, there is a need for objective TMO parameter selection to automate the rendering process. To that end, we investigate into a new objective method for TMO parameters optimization. Our method is based on quantification of contrast reversal and naturalness. As an important advantage, it does not require any prior knowledge about the input HDR image and works independently on the used TMO. Experimental results using a variety of HDR images and several popular TMOs demonstrate the value of our method in comparison to default TMO parameter settings.
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.
Proc. SPIE. 9138, Optics, Photonics, and Digital Technologies for Multimedia Applications III
KEYWORDS: Lithium, Visualization, Video surveillance, Surveillance, Range imaging, High dynamic range imaging, Information security, Information visualization, Time multiplexed optical shutter, Visibility
High Dynamic Range (HDR) imaging has been gaining popularity in recent years. Different from the traditional low dynamic range (LDR), HDR content tends to be visually more appealing and realistic as it can represent the dynamic range of the visual stimuli present in the real world. As a result, more scene details can be faithfully reproduced. As a direct consequence, the visual quality tends to improve. HDR can be also directly exploited for new applications such as video surveillance and other security tasks. Since more scene details are available in HDR, it can help in identifying/tracking visual information which otherwise might be difficult with typical LDR content due to factors such as lack/excess of illumination, extreme contrast in the scene, etc. On the other hand, with HDR, there might be issues related to increased privacy intrusion. To display the HDR content on the regular screen, tone-mapping operators (TMO) are used. In this paper, we present the universal method for TMO parameters tuning, in order to maintain as many details as possible, which is desirable in security applications. The method’s performance is verified on several TMOs by comparing the outcomes from tone-mapping with default and optimized parameters. The results suggest that the proposed approach preserves more information which could be of advantage for security surveillance but, on the other hand, makes us consider possible increase in privacy intrusion.
High Dynamic Range (HDR) signals capture much higher contrasts as compared to the traditional 8-bit low dynamic
range (LDR) signals. This is achieved by representing the visual signal via values that are related to the real-world
luminance, instead of gamma encoded pixel values which is the case with LDR. Therefore, HDR signals cover a larger
luminance range and tend to have more visual appeal. However, due to the higher luminance conditions, the existing
methods cannot be directly employed for objective quality assessment of HDR signals. For that reason, the HDR Visual
Difference Predictor (HDR-VDP-2) has been proposed. HDR-VDP-2 is primarily a visibility prediction metric i.e.
whether the signal distortion is visible to the eye and to what extent. Nevertheless, it also employs a pooling function to
compute an overall quality score. This paper focuses on the pooling aspect in HDR-VDP-2 and employs a
comprehensive database of HDR images (with their corresponding subjective ratings) to improve the prediction accuracy
of HDR-VDP-2. We also discuss and evaluate the existing objective methods and provide a perspective towards better
HDR quality assessment.
We study the issue of quality assessment in tone mapping-based high-dynamic-range (HDR) image compression. In this, there are two stages at which a decision should be made regarding perceptual visual quality: (a) for finding the optimal parameters of the dynamic range reduction function so that the visual quality is maximized, and (b) visual quality judgment of the decompressed image. We first investigate two objective optimization criteria, namely mean squared error and structural similarity index measure, toward optimization of a tone mapping model-based HDR image compression method. We then conduct a comprehensive subjective study to evaluate the visual quality of the compressed HDR images. Therefore, we consider both objective and subjective aspects for HDR image compression. To our knowledge, no systematic and comprehensive studies exist in the current literature which shed light on the issue of quality assessment in HDR compression. So this study brings in new knowledge and perspective for the relatively less investigated topic of HDR compression from the view point of perceptual quality. We further evaluate the prediction performances of four objective methods on the 140 compressed HDR images that have been subjectively rated.
High Dynamic Range (HDR) images/videos require the use of a tone mapping operator (TMO) when visualized on Low Dynamic Range (LDR) displays. From an artistic intention point of view, TMOs are not necessarily transparent and might induce different behavior to view the content. In this paper, we investigate and quantify how TMOs modify visual attention (VA). To that end both objective and subjective tests in the form of eye-tracking experiments have been conducted on several still image content that have been processed by 11 different TMOs. Our studies confirm that TMOs can indeed modify human attention and fixation behavior significantly. Therefore our studies suggest that VA needs consideration for evaluating the overall perceptual impact of TMOs on HDR content. Since the existing studies so far have only considered the quality or aesthetic appeal angle, this study brings in a new perspective regarding the importance of VA in HDR content processing for visualization on LDR displays.
Image quality assessment (IQA) is useful in many visual processing systems but challenging to perform in line with the
human perception. A great deal of recent research effort has been directed towards IQA. In order to overcome the
difficulty and infeasibility of subjective tests in many situations, the aim of such effort is to assess visual quality
objectively towards better alignment with the perception of the Human Visual system (HVS). In this work, we review
and analyze the recent progress in the areas related to IQA, as well as giving our views whenever possible. Following the
recent trends, we discuss the engineering approach in more details, explore the related aspects for feature pooling, and
present a case study with machine learning.