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Journal ArticleDOI

Preference of Experience in Image Tone-Mapping: Dataset and Framework for Objective Measures Comparison

TL;DR: A subjective experiment attempting to determine users’ preference with respect to these two types of content in two different viewing scenarios—with and without the HDR reference shows that the absence of the reference can significantly influence the subjects' preferences for the natural images, while no significant impact has been found in the case of the synthetic images.
Abstract: The popularity of high dynamic range (HDR) imaging has grown in both academic and private research sectors. Since the native visualization of HDR content still has its limitations, the importance of dynamic range compression (i.e., tone-mapping) is very high. This paper evaluates observers’ preference of experience in context of image tone-mapping. Given the different nature of natural and computer-generated content, the way observers perceive the quality of tone-mapped images can be fundamentally different. In this paper, we describe a subjective experiment attempting to determine users’ preference with respect to these two types of content in two different viewing scenarios—with and without the HDR reference. The results show that the absence of the reference can significantly influence the subjects’ preferences for the natural images, while no significant impact has been found in the case of the synthetic images. Moreover, we introduce a benchmarking framework and compare the performance of selected objective metrics. The resulting dataset and framework are made publicly available to provide a common test bed and methodology for evaluating metrics in the considered scenario.
Citations
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Journal ArticleDOI
TL;DR: Experimental simulation results obtained from two large SCI databases have shown that the proposed GFM model yields a higher consistency with the human perception on the assessment of SCIs but also requires a lower computational complexity, compared with that of classical and state-of-the-art IQA models.
Abstract: In this paper, an accurate and efficient full-reference image quality assessment (IQA) model using the extracted Gabor features, called Gabor feature-based model (GFM), is proposed for conducting objective evaluation of screen content images (SCIs). It is well-known that the Gabor filters are highly consistent with the response of the human visual system (HVS), and the HVS is highly sensitive to the edge information. Based on these facts, the imaginary part of the Gabor filter that has odd symmetry and yields edge detection is exploited to the luminance of the reference and distorted SCI for extracting their Gabor features, respectively. The local similarities of the extracted Gabor features and two chrominance components, recorded in the LMN color space, are then measured independently. Finally, the Gabor-feature pooling strategy is employed to combine these measurements and generate the final evaluation score. Experimental simulation results obtained from two large SCI databases have shown that the proposed GFM model not only yields a higher consistency with the human perception on the assessment of SCIs but also requires a lower computational complexity, compared with that of classical and state-of-the-art IQA models. 1 1 The source code for the proposed GFM will be available at http://smartviplab.org/pubilcations/GFM.html .

93 citations


Additional excerpts

  • ...[43]–[45] can not be employed to perform statistical significance test in this work....

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Journal ArticleDOI
TL;DR: This paper builds a compressed VR image quality (CVIQ) database, and proposes a multi-channel convolution neural network (CNN) for blind 360-degree image quality assessment (MC360IQA), which achieves the best performance among the state-of-art full-reference and no-reference image quality Assessment (IQA) models on the CVIQ database and other available360-degree IQA database.
Abstract: 360-degree images/videos have been dramatically increasing in recent years. The characteristic of omnidirectional-view results in high resolution of 360-degree images/videos, which makes them difficult to be transported and stored. To deal with the problem, video coding technologies are used to compress the omnidirectional content but they will introduce the compression distortion. Therefore, it is important to study how popular coding technologies affect the quality of 360-degree images. In this paper, we present a study on both subjective and objective quality assessment of compressed virtual reality (VR) images. We first build a compressed VR image quality (CVIQ) database including 16 reference images and 528 compressed ones with three prevailing coding technologies. Then, we propose a multi-channel convolution neural network (CNN) for blind 360-degree image quality assessment (MC360IQA). To be consistent with the visual content seen in the VR device, we project each 360-degree image into six viewport images, which are adopted as inputs of the proposed model. MC360IQA consists of two parts, a multi-channel CNN and an image quality regressor. The multi-channel CNN includes six parallel hyper-ResNet34 networks, where the hyper structure is used to incorporate the features from intermediate layers. The image quality regressor fuses the features and regresses them to final scores. The experimental results show that our model achieves the best performance among the state-of-art full-reference (FR) and no-reference (NR) image quality assessment (IQA) models on the CVIQ database and other available 360-degree IQA database.

91 citations


Cites background from "Preference of Experience in Image T..."

  • ...[70]–[73], which evaluates classification abilities of IQA models to distinguish which of the two images is better or of the...

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Journal ArticleDOI
TL;DR: In this article, screen content, which is often computer-generated, has many characteristics distinctly different from conventional camera-captured natural scene content, and such characteristic differences impose majo...
Abstract: Screen content, which is often computer-generated, has many characteristics distinctly different from conventional camera-captured natural scene content. Such characteristic differences impose majo...

54 citations

Journal ArticleDOI
Ting Luo1, Gangyi Jiang1, Mei Yu1, Haiyong Xu1, Wei Gao1 
TL;DR: The experimental results show that the proposed method can efficiently resist different TMOs and common image attacks, outperforming other existing HDR image watermarking methods.

25 citations

Journal ArticleDOI
TL;DR: The focus of this contribution is the development of a QoE assessment framework, in line with the latest standardization progress in the field of QOE assessment, for understanding the visual effect of asymmetric and symmetric encoding for immersive media.
Abstract: The assessment of Quality of Experience (QoE) for stereoscopic 3-D video is a challenging task, especially in 3-D video compression and transmission applications. The focus of this contribution is the development of a QoE assessment framework, in line with the latest standardization progress in the field of QoE assessment, for understanding the visual effect of asymmetric and symmetric encoding for immersive media. Asymmetric stereoscopic video coding exploits the binocular suppression of the human vision system by representing one of the two views with a lower quality. This processing, while of limited effects on image quality, may influence the overall QoE. Many studies show that the QoE of immersive media such as 3-DTV can be thought as the combination of the perceived visual quality, the perceived depth quality, the visual fatigue, and visual discomfort. In this paper, we aim at: 1) exploiting the concept of preference of experience and protocols recently standardized for characterizing QoE; 2) conducting a case study using these standardized protocols to investigate the factors involving visual discomfort in stereoscopic video sequences with a focus on binocular rivalry; and 3) presenting the results of subjective experiments performed, by using the perceptual quality and preference of experience assessment protocols, for evaluating the impact of symmetrical, asymmetrical, and alternate coding schemes.

16 citations


Additional excerpts

  • ...Preference of Experience (PoE) [15] and employed beyond 3DTV in context such as HDR QoE assessment [16]....

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References
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Journal ArticleDOI
TL;DR: A completely blind IQA model that uses features derived from an image’s contourlet transform and singular-value decomposition to build algorithms that can predict image quality without any training or any prior knowledge of the images or their distortions is developed.
Abstract: Most current state-of-the-art blind image quality assessment (IQA) algorithms usually require process training or learning. Here, we have developed a completely blind IQA model that uses features derived from an image’s contourlet transform and singular-value decomposition. The model is used to build algorithms that can predict image quality without any training or any prior knowledge of the images or their distortions. The new method consists of three steps: first, the contourlet transform is used on the image to obtain detailed high-frequency structural information from the image; second, the singular values of the just-obtained “structural image” are computed; and finally, two new universal blind IQA indices are constructed utilizing the area and slope of the truncated singular-value curves of the “structural image.” Experimental results on three open databases show that the proposed algorithms deliver quality predictions that have high correlations against human subjective judgments and are highly competitive with the state-of-the-art.

5 citations

Proceedings ArticleDOI
30 Jul 2015
TL;DR: This paper uses established models of camera noise and human contrast perception to design two new quality scores: contrast waste and contrast loss, which measure image quality as a function of contrast allocation and proposes a new noise-aware tone curve.
Abstract: Existing tone mapping operators (TMOs) provide good results in well-lit scenes, but often perform poorly on im-ages in low light conditions. In these scenes, noise is prevalent and gets amplified by TMOs, as they confuse contrast created by noise with contrast created by the scene. This paper presents a principled approach to produce tone mapped images with less visible noise. For this purpose, we leverage established models of camera noise and human contrast perception to design two new quality scores: contrast waste and contrast loss, which measure image quality as a function of contrast allocation. To produce tone mappings with less visible noise, we apply these scores in two ways: first, to automatically tune the parameters of existing TMOs to reduce the amount of noise they produce; and second, to propose a new noise-aware tone curve

5 citations


"Preference of Experience in Image T..." refers background in this paper

  • ...Further statistical analysis also suggested that the factor of influence was indeed the presence of the HDR display....

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Proceedings ArticleDOI
19 May 2013
TL;DR: Experimental results confirm that the proposed adaptive solution always improves upon the contrast achieved from whatever given T MO parameter settings in the tested images, so it helps to achieve the results of a more optimal TMO parameter setting without the human input.
Abstract: Tone mapping operators (TMOs) employed to visualize high dynamic range (HDR) content on conventional low dynamic range (LDR) devices suffer from two major drawbacks. First, none of them can faithfully reproduce all the contrast present in HDR images. Second, most of them require one or more parameters which are mostly content specific and their optimal values can be set only via subjective testing. To address these issues, this paper proposes that `quality driven' adaptive contrast enhancement is a practical solution. This is achieved by enhancing the contrast adaptively based on the loss of contrast between the HDR and tone mapped image. Experimental results confirm that the proposed adaptive solution always improves upon the contrast achieved from whatever given TMO parameter settings in the tested images. So it helps to achieve the results of a more optimal TMO parameter setting without the human input.

2 citations


"Preference of Experience in Image T..." refers result in this paper

  • ...Hence, our studies provide valuable insights towards this goal....

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